diff --git a/.copier-answers.yml b/.copier-answers.yml
index 5743335a..e8de2028 100644
--- a/.copier-answers.yml
+++ b/.copier-answers.yml
@@ -1,6 +1,6 @@
 # WARNING: Do not edit this file manually.
 # Any changes will be overwritten by Copier.
-_commit: v0.10.1-41-g508666e
+_commit: v0.11.3
 _src_path: gh:easyscience/templates
 app_docs_url: https://easyscience.github.io/dynamics-app
 app_doi: 10.5281/zenodo.18877180
diff --git a/.github/actions/download-artifact/action.yml b/.github/actions/download-artifact/action.yml
index e4fd62f5..d1fff1a0 100644
--- a/.github/actions/download-artifact/action.yml
+++ b/.github/actions/download-artifact/action.yml
@@ -1,5 +1,5 @@
 name: 'Download artifact'
-description: 'Generic wrapper for actions/download-artifact'
+description: 'Wrapper for actions/download-artifact'
 inputs:
   name:
     description: 'Name of the artifact to download'
@@ -39,7 +39,7 @@ runs:
   using: 'composite'
   steps:
     - name: Download artifact
-      uses: actions/download-artifact@v4
+      uses: actions/download-artifact@v8
       with:
         name: ${{ inputs.name }}
         path: ${{ inputs.path }}
diff --git a/.github/actions/github-script/action.yml b/.github/actions/github-script/action.yml
index ab32da56..50de89b7 100644
--- a/.github/actions/github-script/action.yml
+++ b/.github/actions/github-script/action.yml
@@ -13,7 +13,7 @@ inputs:
 runs:
   using: 'composite'
   steps:
-    - uses: actions/github-script@v8
+    - uses: actions/github-script@v9
       with:
         script: ${{ inputs.script }}
         github-token: ${{ inputs.github-token }}
diff --git a/.github/actions/setup-easyscience-bot/action.yml b/.github/actions/setup-easyscience-bot/action.yml
index 4b28eaf8..e51eb01a 100644
--- a/.github/actions/setup-easyscience-bot/action.yml
+++ b/.github/actions/setup-easyscience-bot/action.yml
@@ -22,9 +22,9 @@ runs:
   steps:
     - name: Create GitHub App installation token
       id: app-token
-      uses: actions/create-github-app-token@v2
+      uses: actions/create-github-app-token@v3
       with:
-        app-id: ${{ inputs.app-id }}
+        client-id: ${{ inputs.app-id }}
         private-key: ${{ inputs.private-key }}
         repositories: ${{ inputs.repositories }}
 
diff --git a/.github/actions/setup-pixi/action.yml b/.github/actions/setup-pixi/action.yml
index 167ee623..dc3232c6 100644
--- a/.github/actions/setup-pixi/action.yml
+++ b/.github/actions/setup-pixi/action.yml
@@ -1,5 +1,5 @@
 name: 'Setup Pixi Environment'
-description: 'Sets up pixi with common configuration'
+description: 'Wrapper for prefix-dev/setup-pixi'
 inputs:
   environments:
     description: 'Pixi environments to setup'
@@ -33,7 +33,7 @@ inputs:
 runs:
   using: 'composite'
   steps:
-    - uses: prefix-dev/setup-pixi@v0.9.4
+    - uses: prefix-dev/setup-pixi@v0.9.5
       with:
         environments: ${{ inputs.environments }}
         activate-environment: ${{ inputs.activate-environment }}
diff --git a/.github/actions/upload-artifact/action.yml b/.github/actions/upload-artifact/action.yml
index 825ac396..fe8e4680 100644
--- a/.github/actions/upload-artifact/action.yml
+++ b/.github/actions/upload-artifact/action.yml
@@ -1,5 +1,5 @@
 name: 'Upload artifact'
-description: 'Generic wrapper for actions/upload-artifact'
+description: 'Wrapper for actions/upload-artifact'
 inputs:
   name:
     description: 'Artifact name'
@@ -38,7 +38,7 @@ runs:
   using: 'composite'
   steps:
     - name: Upload artifact
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v7
       with:
         name: ${{ inputs.name }}
         path: ${{ inputs.path }}
diff --git a/.github/actions/upload-codecov/action.yml b/.github/actions/upload-codecov/action.yml
index 37d6298a..0cb15d1f 100644
--- a/.github/actions/upload-codecov/action.yml
+++ b/.github/actions/upload-codecov/action.yml
@@ -1,5 +1,5 @@
 name: 'Upload coverage to Codecov'
-description: 'Generic wrapper for codecov/codecov-action@v5'
+description: 'Wrapper for codecov/codecov-action'
 
 inputs:
   name:
@@ -32,7 +32,7 @@ inputs:
 runs:
   using: composite
   steps:
-    - uses: codecov/codecov-action@v5
+    - uses: codecov/codecov-action@v6
       with:
         name: ${{ inputs.name }}
         flags: ${{ inputs.flags }}
diff --git a/.github/scripts/publish-dashboard.sh b/.github/scripts/publish-dashboard.sh
new file mode 100644
index 00000000..dcbd0b32
--- /dev/null
+++ b/.github/scripts/publish-dashboard.sh
@@ -0,0 +1,77 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+remote_repository="${DASHBOARD_REMOTE_REPOSITORY:?}"
+publish_branch="${DASHBOARD_PUBLISH_BRANCH:-master}"
+source_dir="${DASHBOARD_SOURCE_DIR:?}"
+token="${DASHBOARD_TOKEN:?}"
+git_user_name="${DASHBOARD_GIT_USER_NAME:-easyscience[bot]}"
+git_user_email="${DASHBOARD_GIT_USER_EMAIL:?}"
+commit_message="${DASHBOARD_COMMIT_MESSAGE:?}"
+max_attempts="${DASHBOARD_PUSH_ATTEMPTS:-3}"
+delay_seconds="${DASHBOARD_PUSH_DELAY_SECONDS:-15}"
+
+workspace_dir="$(mktemp -d)"
+repo_dir="${workspace_dir}/dashboard"
+remote_url="https://x-access-token:${token}@github.com/${remote_repository}.git"
+
+cleanup() {
+  rm -rf "${workspace_dir}"
+}
+
+prepare_worktree() {
+  if [[ ! -d "${repo_dir}/.git" ]]; then
+    git clone --branch "${publish_branch}" --depth 1 "${remote_url}" "${repo_dir}"
+  else
+    git -C "${repo_dir}" fetch origin "${publish_branch}"
+    git -C "${repo_dir}" checkout "${publish_branch}"
+    git -C "${repo_dir}" reset --hard "origin/${publish_branch}"
+    git -C "${repo_dir}" clean -fd
+  fi
+
+  git -C "${repo_dir}" config user.name "${git_user_name}"
+  git -C "${repo_dir}" config user.email "${git_user_email}"
+}
+
+sync_publish_dir() {
+  cp -R "${source_dir}/." "${repo_dir}/"
+  git -C "${repo_dir}" add .
+
+  if git -C "${repo_dir}" diff --cached --quiet; then
+    return 1
+  fi
+
+  git -C "${repo_dir}" commit -m "${commit_message}"
+}
+
+trap cleanup EXIT
+
+prepare_worktree
+
+if ! sync_publish_dir; then
+  echo "No dashboard changes to publish."
+  exit 0
+fi
+
+for ((attempt = 1; attempt <= max_attempts; attempt += 1)); do
+  if git -C "${repo_dir}" push origin "HEAD:${publish_branch}"; then
+    echo "Dashboard published on attempt ${attempt}."
+    exit 0
+  fi
+
+  if ((attempt == max_attempts)); then
+    echo "Dashboard publish failed after ${max_attempts} attempts." >&2
+    exit 1
+  fi
+
+  echo "Dashboard push attempt ${attempt} failed. Retrying in ${delay_seconds}s." >&2
+  sleep "${delay_seconds}"
+
+  prepare_worktree
+
+  if ! sync_publish_dir; then
+    echo "Dashboard changes already exist in the target repository."
+    exit 0
+  fi
+done
\ No newline at end of file
diff --git a/.github/workflows/backmerge.yml b/.github/workflows/backmerge.yml
index 47b3384a..36ce6f54 100644
--- a/.github/workflows/backmerge.yml
+++ b/.github/workflows/backmerge.yml
@@ -27,7 +27,7 @@ jobs:
 
     steps:
       - name: Checkout repository (for local actions)
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Setup easyscience[bot]
         id: bot
@@ -38,7 +38,7 @@ jobs:
           repositories: ${{ github.event.repository.name }}
 
       - name: Checkout repository (with bot token)
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
           token: ${{ steps.bot.outputs.token }}
diff --git a/.github/workflows/cleanup.yml b/.github/workflows/cleanup.yml
index 7305679b..21c72b38 100644
--- a/.github/workflows/cleanup.yml
+++ b/.github/workflows/cleanup.yml
@@ -15,11 +15,11 @@ on:
       days:
         description: 'Number of days.'
         required: true
-        default: 30
+        default: '30'
       minimum_runs:
         description: 'The minimum runs to keep for each workflow.'
         required: true
-        default: 6
+        default: '6'
       delete_workflow_pattern:
         description:
           'The name or filename of the workflow. if not set then it will target all
diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml
index e1e44d41..d96e5b87 100644
--- a/.github/workflows/coverage.yml
+++ b/.github/workflows/coverage.yml
@@ -5,8 +5,6 @@ on:
   push:
     branches:
       - develop
-    # Do not run on version tags (those are handled by other workflows)
-    tags-ignore: ['v*']
   # Trigger the workflow on pull request
   pull_request:
   # Allows you to run this workflow manually from the Actions tab
@@ -34,7 +32,7 @@ jobs:
 
     steps:
       - name: Check-out repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
@@ -48,7 +46,7 @@ jobs:
 
     steps:
       - name: Check-out repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
@@ -71,7 +69,7 @@ jobs:
 
     steps:
       - name: Check-out repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
diff --git a/.github/workflows/dashboard.yml b/.github/workflows/dashboard.yml
index df89dfb7..9d1f2b0b 100644
--- a/.github/workflows/dashboard.yml
+++ b/.github/workflows/dashboard.yml
@@ -7,6 +7,10 @@ on:
 permissions:
   contents: read
 
+concurrency:
+  group: dashboard-publish-${{ github.repository }}
+  cancel-in-progress: false
+
 # Set the environment variables to be used in all jobs defined in this workflow
 env:
   CI_BRANCH: ${{ github.head_ref || github.ref_name }}
@@ -21,7 +25,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
@@ -44,19 +48,18 @@ jobs:
 
             git worktree add ../$BRANCH origin/$BRANCH
             mkdir -p reports/$BRANCH
-            CODE_PATH="../$BRANCH/src"
 
             echo "Docstring coverage for branch $BRANCH"
-            pixi run interrogate -c pyproject.toml --fail-under=0 $CODE_PATH > reports/$BRANCH/coverage-docstring.txt
+            pixi run interrogate -c pyproject.toml --fail-under=0 ../$BRANCH/src > reports/$BRANCH/coverage-docstring.txt
 
             echo "Cyclomatic complexity for branch $BRANCH"
-            pixi run radon cc -s -j $CODE_PATH > reports/$BRANCH/cyclomatic-complexity.json
+            pixi run radon cc -s -j ../$BRANCH/src > reports/$BRANCH/cyclomatic-complexity.json
 
             echo "Maintainability index for branch $BRANCH"
-            pixi run radon mi -j $CODE_PATH > reports/$BRANCH/maintainability-index.json
+            pixi run radon mi -j ../$BRANCH/src > reports/$BRANCH/maintainability-index.json
 
             echo "Raw metrics for branch $BRANCH"
-            pixi run radon raw -s -j $CODE_PATH > reports/$BRANCH/raw-metrics.json
+            pixi run radon raw -s -j ../$BRANCH/src > reports/$BRANCH/raw-metrics.json
           done
 
       - name: Generate dashboard HTML
@@ -82,22 +85,25 @@ jobs:
             ${{ github.event.repository.name }}
             dashboard
 
-      # Publish to external dashboard repository with retry logic.
+      # Push to external dashboard repository with retry logic.
       # Retry is needed to handle transient GitHub API/authentication issues
       # that occasionally cause 403 errors when multiple workflows push concurrently.
       # Uses personal_token (not github_token) as GITHUB_TOKEN cannot access external repos.
-      - name: Publish to main branch of ${{ github.repository }}
-        uses: Wandalen/wretry.action@v3.8.0
-        with:
-          attempt_limit: 3
-          attempt_delay: 15000 # 15 seconds between retries
-          action: peaceiris/actions-gh-pages@v4
-          with: |
-            publish_dir: ./_dashboard_publish
-            keep_files: true
-            external_repository: ${{ env.REPO_OWNER }}/dashboard
-            publish_branch: master
-            personal_token: ${{ steps.bot.outputs.token }}
+      - name:
+          Push to ${{ env.REPO_OWNER }}/dashboard/${{ env.REPO_NAME }}/${{ env.CI_BRANCH
+          }}
+        shell: bash
+        env:
+          DASHBOARD_COMMIT_MESSAGE: ${{ env.CI_BRANCH }}
+          DASHBOARD_GIT_USER_EMAIL:
+            ${{ vars.EASYSCIENCE_APP_ID }}+easyscience[bot]@users.noreply.github.com
+          DASHBOARD_PUSH_ATTEMPTS: '3'
+          DASHBOARD_PUSH_DELAY_SECONDS: '15'
+          DASHBOARD_PUBLISH_BRANCH: master
+          DASHBOARD_REMOTE_REPOSITORY: ${{ env.REPO_OWNER }}/dashboard
+          DASHBOARD_SOURCE_DIR: ./_dashboard_publish
+          DASHBOARD_TOKEN: ${{ steps.bot.outputs.token }}
+        run: bash ./.github/scripts/publish-dashboard.sh
 
       - name: Add dashboard link to summary
         run: |
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 52ccd8e2..2f1a413e 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -27,12 +27,16 @@ on:
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
-# Allow only one concurrent deployment to gh-pages at a time.
-# All docs workflows share the same concurrency group to prevent race conditions
-# when multiple branches/tags trigger simultaneous deployments.
+# - Non-tagged pushes and pull requests all use `docs-dev` group, so
+#   they cancel each other.
+# - Tagged pushes use their own group like docs-v1.2.3, so they do not
+#   cancel non-tagged runs, and non-tagged runs do not cancel them.
 concurrency:
-  group: docs-gh-pages-deploy
-  cancel-in-progress: false
+  group: >-
+    ${{ startsWith(github.ref, 'refs/tags/v')
+      && format('docs-{0}', github.ref_name)
+      || 'docs-dev' }}
+  cancel-in-progress: true
 
 # Set the environment variables to be used in all jobs defined in this workflow
 env:
@@ -83,7 +87,7 @@ jobs:
       # Check out the repository source code.
       # Note: The gh-pages branch is fetched separately later for mike deployment.
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       # Activate dark mode to create documentation with Plotly charts in dark mode
       # Need a better solution to automatically switch the chart colour theme based on the mkdocs material switcher
diff --git a/.github/workflows/issues-labels.yml b/.github/workflows/issues-labels.yml
index df3c55f1..4fbf4fab 100644
--- a/.github/workflows/issues-labels.yml
+++ b/.github/workflows/issues-labels.yml
@@ -1,6 +1,5 @@
-# Verifies if an issue has at least one of the `[scope]` and one of the
-# `[priority]` labels. If not, the bot adds labels with a warning emoji
-# to indicate that those labels need to be added.
+# Verifies if the current issue has at least one `[scope]` label and one
+# `[priority]` label. If either is missing, the workflow adds a reminder label.
 
 name: Issue labels check
 
@@ -13,8 +12,6 @@ permissions:
 
 jobs:
   check-labels:
-    if: github.actor != 'easyscience[bot]'
-
     runs-on: ubuntu-latest
 
     concurrency:
@@ -22,30 +19,22 @@ jobs:
       cancel-in-progress: true
 
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@v5
-
-      - name: Setup easyscience[bot]
-        id: bot
-        uses: ./.github/actions/setup-easyscience-bot
+      - name: Ensure [scope] label
+        uses: Rindrics/expect-label-prefix@v1.2.1
         with:
-          app-id: ${{ vars.EASYSCIENCE_APP_ID }}
-          private-key: ${{ secrets.EASYSCIENCE_APP_KEY }}
-
-      - name: Check for required [scope] label
-        uses: trstringer/require-label-prefix@v1
-        with:
-          secret: ${{ steps.bot.outputs.token }}
-          prefix: '[scope]'
-          labelSeparator: ' '
-          addLabel: true
-          defaultLabel: '[scope] ⚠️ label needed'
-
-      - name: Check for required [priority] label
-        uses: trstringer/require-label-prefix@v1
+          repository_full_name: ${{ github.repository }}
+          token: ${{ github.token }}
+          label_prefix: '[scope]'
+          label_separator: ' '
+          add_label: 'true'
+          default_label: '[scope] ⚠️ label needed'
+
+      - name: Ensure [priority] label
+        uses: Rindrics/expect-label-prefix@v1.2.1
         with:
-          secret: ${{ steps.bot.outputs.token }}
-          prefix: '[priority]'
-          labelSeparator: ' '
-          addLabel: true
-          defaultLabel: '[priority] ⚠️ label needed'
+          repository_full_name: ${{ github.repository }}
+          token: ${{ github.token }}
+          label_prefix: '[priority]'
+          label_separator: ' '
+          add_label: 'true'
+          default_label: '[priority] ⚠️ label needed'
diff --git a/.github/workflows/lint-format.yml b/.github/workflows/lint-format.yml
index f1135fa5..16dd95c9 100644
--- a/.github/workflows/lint-format.yml
+++ b/.github/workflows/lint-format.yml
@@ -41,7 +41,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
diff --git a/.github/workflows/pr-labels.yml b/.github/workflows/pr-labels.yml
index 642cd318..fcb8a78c 100644
--- a/.github/workflows/pr-labels.yml
+++ b/.github/workflows/pr-labels.yml
@@ -1,14 +1,9 @@
 # Verifies if a pull request has at least one label from a set of valid
 # labels before it can be merged.
 #
-# NOTE:
-# This workflow may be triggered twice in quick succession when a PR is
-# created:
-#   1) `opened`  — when the pull request is initially created
-#   2) `labeled` — if labels are added immediately after creation
-# (e.g. by manual labeling, another workflow, or GitHub App).
-#
-# These are separate GitHub events, so two workflow runs can be started.
+# The label validation is delegated to `mheap/github-action-required-labels`,
+# which checks the current PR labels via the GitHub API and can add or update a
+# PR comment when the required label set is missing.
 
 name: PR labels check
 
@@ -17,40 +12,34 @@ on:
     types: [opened, labeled, unlabeled, synchronize]
 
 permissions:
-  pull-requests: read
+  issues: write
+  pull-requests: write
 
 jobs:
   check-labels:
     runs-on: ubuntu-latest
 
     steps:
-      - name: Check for valid labels
-        run: |
-          PR_LABELS=$(echo '${{ toJson(github.event.pull_request.labels.*.name) }}' | jq -r '.[]')
-
-          echo "Current PR labels: $PR_LABELS"
-          VALID_LABELS=(
-            "[bot] release"
-            "[scope] bug"
-            "[scope] documentation"
-            "[scope] enhancement"
-            "[scope] maintenance"
-            "[scope] significant"
-          )
-
-          found=false
-          for label in "${VALID_LABELS[@]}"; do
-            if echo "$PR_LABELS" | grep -Fxq "$label"; then
-              echo "✅ Found valid label: $label"
-              found=true
-              break
-            fi
-          done
-
-          if [ "$found" = false ]; then
-            echo "ERROR: PR must have at least one of the following labels:"
-            for label in "${VALID_LABELS[@]}"; do
-              echo "       - $label"
-            done
-            exit 1
-          fi
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Setup easyscience[bot]
+        id: bot
+        uses: ./.github/actions/setup-easyscience-bot
+        with:
+          app-id: ${{ vars.EASYSCIENCE_APP_ID }}
+          private-key: ${{ secrets.EASYSCIENCE_APP_KEY }}
+
+      - uses: mheap/github-action-required-labels@v5
+        with:
+          token: ${{ steps.bot.outputs.token }}
+          add_comment: true
+          mode: minimum
+          count: 1
+          labels: |
+            [bot] release
+            [scope] bug
+            [scope] documentation
+            [scope] enhancement
+            [scope] maintenance
+            [scope] significant
diff --git a/.github/workflows/pypi-publish.yml b/.github/workflows/pypi-publish.yml
index 4839c4d1..37a60e01 100644
--- a/.github/workflows/pypi-publish.yml
+++ b/.github/workflows/pypi-publish.yml
@@ -20,7 +20,7 @@ jobs:
 
     steps:
       - name: Check-out repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0 # full history with tags to get the version number by versioningit
 
diff --git a/.github/workflows/pypi-test.yml b/.github/workflows/pypi-test.yml
index 7224f1e4..b9193754 100644
--- a/.github/workflows/pypi-test.yml
+++ b/.github/workflows/pypi-test.yml
@@ -32,7 +32,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
@@ -53,6 +53,10 @@ jobs:
         working-directory: easydynamics
         run: pixi add "python=3.14"
 
+      - name: Add other Conda dependencies
+        working-directory: easydynamics
+        run: pixi add gsl
+
       - name: Add easydynamics (with dev dependencies) from PyPI
         working-directory: easydynamics
         run: pixi add --pypi "easydynamics[dev]"
diff --git a/.github/workflows/release-notes.yml b/.github/workflows/release-notes.yml
index be331fe0..fc4b8fc9 100644
--- a/.github/workflows/release-notes.yml
+++ b/.github/workflows/release-notes.yml
@@ -20,7 +20,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0 # full history with tags to get the version number
 
@@ -61,7 +61,7 @@ jobs:
           GITHUB_TOKEN: ${{ steps.bot.outputs.token }}
 
       - name: Create GitHub draft release
-        uses: softprops/action-gh-release@v2
+        uses: softprops/action-gh-release@v3
         with:
           draft: true
           tag_name: ${{ steps.draft.outputs.tag_name }}
diff --git a/.github/workflows/release-pr.yml b/.github/workflows/release-pr.yml
index 7e6fda49..3fa073b9 100644
--- a/.github/workflows/release-pr.yml
+++ b/.github/workflows/release-pr.yml
@@ -30,7 +30,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout ${{ env.SOURCE_BRANCH }} branch
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           ref: ${{ env.SOURCE_BRANCH }}
 
@@ -48,7 +48,7 @@ jobs:
           gh pr create \
             --base ${{ env.DEFAULT_BRANCH }} \
             --head ${{ env.SOURCE_BRANCH }} \
-            --title "Release: merge ${{ env.SOURCE_BRANCH }} into ${{ env.DEFAULT_BRANCH }}" \
+            --title "🎉 Release: merge ${{ env.SOURCE_BRANCH }} into ${{ env.DEFAULT_BRANCH }}" \
             --label "[bot] release" \
             --body "This PR is created automatically to trigger the release pipeline. It merges the accumulated changes from \`${{ env.SOURCE_BRANCH }}\` into \`${{ env.DEFAULT_BRANCH }}\`.
 
diff --git a/.github/workflows/security.yml b/.github/workflows/security.yml
index 9b34cccf..0f43db35 100644
--- a/.github/workflows/security.yml
+++ b/.github/workflows/security.yml
@@ -61,13 +61,13 @@ jobs:
       # focused on the active dev branch.
       - name: Checkout repository (scheduled → develop)
         if: ${{ github.event_name == 'schedule' }}
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           ref: develop
 
       - name: Checkout repository
         if: ${{ github.event_name != 'schedule' }}
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Initialize CodeQL
         uses: github/codeql-action/init@v4
diff --git a/.github/workflows/test-trigger.yml b/.github/workflows/test-trigger.yml
index ecf6b40c..440bae3c 100644
--- a/.github/workflows/test-trigger.yml
+++ b/.github/workflows/test-trigger.yml
@@ -16,7 +16,7 @@ jobs:
 
     steps:
       - name: Checkout develop branch
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           ref: develop
 
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 774edf78..e51ef2e2 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -80,7 +80,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
@@ -184,7 +184,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Download package (incl. extras) from previous job
         uses: ./.github/actions/download-artifact
@@ -229,10 +229,10 @@ jobs:
               exit 1
             fi
 
-            whl_url="file://$(python -c 'import os,sys; print(os.path.abspath(sys.argv[1]))' "${whl_path[0]}")"
+            whl_abs_path="$(python -c 'import os,sys; print(os.path.abspath(sys.argv[1]))' "${whl_path[0]}")"
 
-            echo "Adding easydynamics from: $whl_url"
-            pixi add --pypi "easydynamics[dev] @ ${whl_url}"
+            echo "Adding easydynamics from: $whl_abs_path"
+            pixi add --pypi "easydynamics[dev] @ ${whl_abs_path}"
 
             echo "Exiting pixi project directory"
             cd ..
diff --git a/.github/workflows/tutorial-tests-trigger.yml b/.github/workflows/tutorial-tests-trigger.yml
index 1bc27f4f..4b160d87 100644
--- a/.github/workflows/tutorial-tests-trigger.yml
+++ b/.github/workflows/tutorial-tests-trigger.yml
@@ -16,7 +16,7 @@ jobs:
 
     steps:
       - name: Checkout develop branch
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           ref: develop
 
diff --git a/.github/workflows/tutorial-tests.yml b/.github/workflows/tutorial-tests.yml
index e976e267..a4fc940c 100644
--- a/.github/workflows/tutorial-tests.yml
+++ b/.github/workflows/tutorial-tests.yml
@@ -40,7 +40,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
 
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 168c5d97..87845da0 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -249,6 +249,12 @@ or to run only Python linting checks:
 pixi run py-lint-check
 ```
 
+To add missing license headers:
+
+```bash
+pixi run spdx-add
+```
+
 Some formatting issues can be fixed automatically:
 
 ```bash
diff --git a/docs/docs/tutorials/analysis.ipynb b/docs/docs/tutorials/analysis.ipynb
index 0d30fd4b..2e3bb3d0 100644
--- a/docs/docs/tutorials/analysis.ipynb
+++ b/docs/docs/tutorials/analysis.ipynb
@@ -130,6 +130,16 @@
     "vanadium_analysis.parameters_to_dataset()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "536d0cad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vanadium_analysis.get_parameters_near_bounds()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -338,7 +348,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "easydynamics_newbase",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -352,7 +362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.14.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/tutorials/components.ipynb b/docs/docs/tutorials/components.ipynb
index 55570c35..4934347b 100644
--- a/docs/docs/tutorials/components.ipynb
+++ b/docs/docs/tutorials/components.ipynb
@@ -150,6 +150,30 @@
     "plt.legend()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be79f3fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr = ExpressionComponent(\n",
+    "    'A*erf(B*x)',\n",
+    ")\n",
+    "\n",
+    "expr.A = 1.0\n",
+    "expr.B = 0.5\n",
+    "\n",
+    "\n",
+    "x = np.linspace(-5, 5, 100)\n",
+    "y = expr.evaluate(x)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(x, y, label='erf')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
@@ -168,7 +192,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3.14.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/tutorials/tutorial0_basics.ipynb b/docs/docs/tutorials/tutorial0_basics.ipynb
index 44442036..4f1ad48e 100644
--- a/docs/docs/tutorials/tutorial0_basics.ipynb
+++ b/docs/docs/tutorials/tutorial0_basics.ipynb
@@ -20,10 +20,13 @@
    "source": [
     "# Imports\n",
     "import pooch\n",
+    "import scipp as sc\n",
     "\n",
     "import easydynamics as edyn\n",
     "import easydynamics.sample_model as sm\n",
-    "from easydynamics.analysis.analysis import Analysis\n",
+    "from easydynamics.analysis import Analysis\n",
+    "from easydynamics.analysis import ParameterAnalysis\n",
+    "from easydynamics.analysis.parameter_analysis import FitBinding\n",
     "\n",
     "# Make the plots interactive\n",
     "%matplotlib widget"
@@ -322,7 +325,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(f'The reduced chi-squared value for Q_index=5 is: {fit_result_all_Q[5].reduced_chi}')\n",
+    "print(f'The reduced chi-squared value for Q_index=5 is: {fit_result_all_Q[5].reduced_chi2}')\n",
     "\n",
     "print(f'The minimizer engine is: {fit_result_all_Q[5].minimizer_engine}')"
    ]
@@ -363,12 +366,103 @@
     "analysis.plot_parameters(names=['Gaussian area'])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d6e8ece6",
+   "metadata": {},
+   "source": [
+    "If you wish to customise the plot beyond what is immediately possible with EasyDynamics, you can get the data and model as a scipp datagroup. You may evaluate the model at different energies than the data like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3af8c1f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energy = sc.linspace('energy', -3.5, 3.5, num=1001, unit='meV')\n",
+    "data_and_model = analysis.data_and_model_to_datagroup(energy=energy)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "842c1f01",
    "metadata": {},
    "source": [
-    "It will soon be possible to use **EasyDynamics** to fit these parameters to e.g. a polynomial."
+    "The final step in this tutorial is to fit the are of the `Gaussian` to a straight line. For this, we use the `ParameterAnalysis` class. We create a `Polynomial` with two coefficients for the fit function. We create a `FitBinding`, telling the class we want to fit the parameter named `Gaussian area` with the fit function that we define."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75db3d4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_func = sm.Polynomial(coefficients=[3.7, -0.5], display_name='Straight line')\n",
+    "\n",
+    "binding = FitBinding(parameter_name='Gaussian area', model=fit_func)\n",
+    "\n",
+    "parameter_analysis = ParameterAnalysis(\n",
+    "    parameters=analysis,\n",
+    "    bindings=[binding],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01f45034",
+   "metadata": {},
+   "source": [
+    "Let us plot the start guess using the `plot()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd3cf4a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "634bebdc",
+   "metadata": {},
+   "source": [
+    "It looks decent, so we can fit and plot again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5ba8985",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.fit()\n",
+    "parameter_analysis.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc33728c",
+   "metadata": {},
+   "source": [
+    "To see the parameters we can use the `get_all_parameters()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f18e2944",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.get_all_parameters()"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/tutorial0_more_advanced.ipynb b/docs/docs/tutorials/tutorial0_more_advanced.ipynb
index 49cb8963..ee1e4e57 100644
--- a/docs/docs/tutorials/tutorial0_more_advanced.ipynb
+++ b/docs/docs/tutorials/tutorial0_more_advanced.ipynb
@@ -292,7 +292,65 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "analysis.plot_parameters(names=['Gaussian area', 'Lorentzian area', 'DHO area'])"
+    "analysis.plot_parameters(names=['Gaussian area', 'DHO area', 'DHO center'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eadbd91",
+   "metadata": {},
+   "source": [
+    "With apologies for the lack of creativity, these all appear like straight lines. We can fit them individually or all together using `ParameterAnalysis`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb2f0e06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gauss_fit_func = sm.Polynomial(\n",
+    "    coefficients=[3.7, -0.5], unit='1/angstrom', display_name='Gauss area fit'\n",
+    ")\n",
+    "dho_area_fit_func = sm.Polynomial(\n",
+    "    coefficients=[2.0, 0.12], unit='1/angstrom', display_name='DHO area fit'\n",
+    ")\n",
+    "dho_center_fit_func = sm.Polynomial(\n",
+    "    coefficients=[1.1, 0.2], unit='1/angstrom', display_name='DHO center fit'\n",
+    ")\n",
+    "\n",
+    "binding1 = edyn.FitBinding(parameter_name='Gaussian area', model=gauss_fit_func)\n",
+    "\n",
+    "binding2 = edyn.FitBinding(parameter_name='DHO area', model=dho_area_fit_func)\n",
+    "\n",
+    "binding3 = edyn.FitBinding(parameter_name='DHO center', model=dho_center_fit_func)\n",
+    "\n",
+    "parameter_analysis = edyn.ParameterAnalysis(\n",
+    "    parameters=analysis,\n",
+    "    bindings=[binding1, binding2, binding3],\n",
+    ")\n",
+    "\n",
+    "parameter_analysis.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32bc1efc",
+   "metadata": {},
+   "source": [
+    "The start guesses look reasonable, so we fit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5548093",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.fit()\n",
+    "parameter_analysis.plot()"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/tutorial1_brownian.ipynb b/docs/docs/tutorials/tutorial1_brownian.ipynb
index 413ce036..ea0b90df 100644
--- a/docs/docs/tutorials/tutorial1_brownian.ipynb
+++ b/docs/docs/tutorials/tutorial1_brownian.ipynb
@@ -24,6 +24,8 @@
     "import pooch\n",
     "\n",
     "from easydynamics.analysis.analysis import Analysis\n",
+    "from easydynamics.analysis.parameter_analysis import FitBinding\n",
+    "from easydynamics.analysis.parameter_analysis import ParameterAnalysis\n",
     "from easydynamics.experiment import Experiment\n",
     "from easydynamics.sample_model import BrownianTranslationalDiffusion\n",
     "from easydynamics.sample_model import ComponentCollection\n",
@@ -460,7 +462,7 @@
    "id": "45daa848",
    "metadata": {},
    "source": [
-    "The fit looks good, so now we want to look at the most interesting fit parameters: the width and area of the Lorentzian. In later versions of EasyDynamics it will be possible to fit them to e.g. a DiffusionModel."
+    "The fit looks good, so now we want to look at the most interesting fit parameters: the width and area of the Lorentzian. "
    ]
   },
   {
@@ -470,7 +472,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Let us look at the most interesting fit parameters\n",
     "diffusion_analysis.plot_parameters(names=['Lorentzian width', 'Lorentzian area'])"
    ]
   },
@@ -485,7 +486,105 @@
     "$$\n",
     "where $\\Gamma(Q) = D Q^2$ and $D$ is the diffusion coefficient. $S$ is an overall scale.\n",
     "\n",
-    "In addition to this diffusion model, there is still the elastic incoherent scattering.\n",
+    "To fit the Brownian translational diffusion model to the data, we use the `ParameterAnalysis`. This time, we wish to fit the `Lorentzian`, and we wish to fit both its area (scale, $S$) and width to the diffusion model. We do this by creating a `FitBinding`, saying we want to fit both the area an width of the component called `Lorentzian`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c1ab6b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brownian_diffusion_model = BrownianTranslationalDiffusion(\n",
+    "    display_name='Brownian Translational Diffusion', diffusion_coefficient=2.4e-9, scale=0.5\n",
+    ")\n",
+    "\n",
+    "binding = FitBinding(\n",
+    "    parameter_name='Lorentzian',\n",
+    "    model=brownian_diffusion_model,\n",
+    "    modes=['area', 'width'],\n",
+    ")\n",
+    "\n",
+    "parameter_analysis = ParameterAnalysis(\n",
+    "    parameters=diffusion_analysis,\n",
+    "    bindings=[binding],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53dc12dc",
+   "metadata": {},
+   "source": [
+    "We first plot the start guess to see if it's reasonable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7074c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57b76d06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.plot(names=['Polynomial_c0'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64babb01",
+   "metadata": {},
+   "source": [
+    "This looks pretty good. Let's fit and plot again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26c418ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.fit()\n",
+    "parameter_analysis.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81d30f55",
+   "metadata": {},
+   "source": [
+    "And we can get the parameters of the diffusion model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c47bcd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.get_all_parameters()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc2f8434",
+   "metadata": {},
+   "source": [
+    "This is a good result, but we can do better. Now that we know that the quasielastic scattering is well described by a model of diffusion, we can fit this model directly to the data, fitting all $Q$ simultaneously.\n",
+    "\n",
+    "In addition to this diffusion model, we will still fit the elastic incoherent scattering.\n",
     "\n",
     "We create a new `SampleModel` which as a `DeltaFunction` component for the elastic incoherent scattering and a `BrownianTranslationalDiffusion` diffusion model to describe the rest. We also create a new `BackgroundModel` and `InstrumentModel`."
    ]
@@ -593,7 +692,7 @@
    "id": "bbeec088",
    "metadata": {},
    "source": [
-    "It does not make sense to plot the diffusion parameters, but we can display them (with uncertainties) like this."
+    "It does not make sense to plot the diffusion parameters, as they are just two numbers with uncertainties, but we can display them (with uncertainties) like this."
    ]
   },
   {
@@ -605,6 +704,24 @@
    "source": [
     "diffusion_model.get_all_parameters()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc9bf6b1",
+   "metadata": {},
+   "source": [
+    "For reference, here are the diffusion parameters found from fitting the width and scale of the fitted Lorentzians. Notice that the error bars when fitting everything simultaneously are a lot lower than the two-step fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0af81fa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_analysis.get_all_parameters()"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/docs/tutorials/tutorial2_nanoparticles.ipynb b/docs/docs/tutorials/tutorial2_nanoparticles.ipynb
index 8f4312a1..42eb15ed 100644
--- a/docs/docs/tutorials/tutorial2_nanoparticles.ipynb
+++ b/docs/docs/tutorials/tutorial2_nanoparticles.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "bca91d3c",
    "metadata": {},
    "outputs": [],
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "4fbb90da",
    "metadata": {},
    "outputs": [],
@@ -306,7 +306,7 @@
    "outputs": [],
    "source": [
     "analysis.fit()\n",
-    "analysis.plot_data_and_model(logy=True)"
+    "analysis.plot_data_and_model(logy=True, autoscale=False)"
    ]
   },
   {
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index a7e2be2b..0b97ef25 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -148,8 +148,10 @@ plugins:
         python:
           paths: ['src'] # Change 'src' to your actual sources directory
           options:
+            annotations_path: source
             docstring_style: numpy
-            group_by_category: false
+            group_by_category: true
+            members_order: source
             heading_level: 1
             show_root_heading: true
             show_root_full_path: false
diff --git a/pixi.lock b/pixi.lock
index 0d2efa1f..47a318c6 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -2,6 +2,7 @@ version: 6
 environments:
   default:
     channels:
+    - url: https://conda.anaconda.org/nodefaults/
     - url: https://conda.anaconda.org/conda-forge/
     indexes:
     - https://pypi.org/simple
@@ -19,17 +20,17 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.4.0-py314h680f03e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
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-  name: ruff
-  version: 0.15.9
-  sha256: 2b0c7c341f68adb01c488c3b7d4b49aa8ea97409eae6462d860a79cf55f431b6
-  requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/25/b6/261225b875d7a13b33a6d02508c39c28450b2041bb01d0f7f1a83d569512/ruff-0.15.10-py3-none-win_amd64.whl
+- pypi: https://files.pythonhosted.org/packages/33/f1/9614e03e1cdcbf9437570b5400ced8a720b5db22b28d8e0f1bda429f660d/ruff-0.15.12-py3-none-win_amd64.whl
   name: ruff
-  version: 0.15.10
-  sha256: 28cb32d53203242d403d819fd6983152489b12e4a3ae44993543d6fe62ab42ed
+  version: 0.15.12
+  sha256: c87a162d61ab3adca47c03f7f717c68672edec7d1b5499e652331780fe74950d
   requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/bc/bb/7ddb00a83760ff4a83c4e2fc231fd63937cc7317c10c82f583302e0f6586/ruff-0.15.10-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/aa/a4/f828e9718d3dce1f5f11c39c4f65afd32783c8b2aebb2e3d259e492c47bd/ruff-0.15.12-py3-none-macosx_11_0_arm64.whl
   name: ruff
-  version: 0.15.10
-  sha256: 51cb8cc943e891ba99989dd92d61e29b1d231e14811db9be6440ecf25d5c1609
+  version: 0.15.12
+  sha256: fe87510d000220aa1ed530d4448a7c696a0cae1213e5ec30e5874287b66557b5
   requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/f2/c0/4ac978fe874d0618c7da647862afe697b281c2806f13ce904ad652fa87e4/ruff-0.15.10-py3-none-macosx_11_0_arm64.whl
+- pypi: https://files.pythonhosted.org/packages/e8/39/c61d193b8a1daaa8977f7dea9e8d8ba866e02ea7b65d32f6861693aa4c12/ruff-0.15.12-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
   name: ruff
-  version: 0.15.10
-  sha256: 93cc06a19e5155b4441dd72808fdf84290d84ad8a39ca3b0f994363ade4cebb1
+  version: 0.15.12
+  sha256: 83b2f4f2f3b1026b5fb449b467d9264bf22067b600f7b6f41fc5958909f449d0
   requires_python: '>=3.7'
 - pypi: https://files.pythonhosted.org/packages/0f/0e/0eb94e64f5badef67f11fe1e448dde2a44f00940d8949f4adf71d560552e/scipp-26.3.1-cp314-cp314-macosx_14_0_arm64.whl
   name: scipp
@@ -9610,7 +10170,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 859665
   timestamp: 1774358032165
 - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.5-py314h5bd0f2a_0.conda
@@ -9624,7 +10184,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 912476
   timestamp: 1774358032579
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.5-py312h2bbb03f_0.conda
@@ -9638,7 +10198,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 859155
   timestamp: 1774358568476
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.5-py314h6c2aa35_0.conda
@@ -9667,7 +10227,7 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls:
-  - pkg:pypi/tornado?source=compressed-mapping
+  - pkg:pypi/tornado?source=hash-mapping
   size: 859726
   timestamp: 1774358173994
 - conda: https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.5-py314h5a2d7ad_0.conda
@@ -9685,21 +10245,22 @@ packages:
   - pkg:pypi/tornado?source=hash-mapping
   size: 914835
   timestamp: 1774358183098
-- conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda
-  sha256: f39a5620c6e8e9e98357507262a7869de2ae8cc07da8b7f84e517c9fd6c2b959
-  md5: 019a7385be9af33791c989871317e1ed
+- conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda
+  sha256: dfb681579be59c2e790c95f7f49b7529a9b0511d6385ad276e3c8988cbd54d2c
+  md5: 4bada6a6d908a27262af8ebddf4f7492
   depends:
-  - python >=3.9
+  - python >=3.10
+  - python
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/traitlets?source=hash-mapping
-  size: 110051
-  timestamp: 1733367480074
-- pypi: https://files.pythonhosted.org/packages/bb/4a/2e5583e544bc437d5e8e54b47db87430df9031b29b48d17f26d129fa60c0/trove_classifiers-2026.1.14.14-py3-none-any.whl
+  size: 115165
+  timestamp: 1778074251714
+- pypi: https://files.pythonhosted.org/packages/7b/e3/d81b065a2d866a33a541ac63a2a4cc5737e03ce2379ac3191c98bb8867e3/trove_classifiers-2026.5.7.17-py3-none-any.whl
   name: trove-classifiers
-  version: 2026.1.14.14
-  sha256: 1f9553927f18d0513d8e5ff80ab8980b8202ce37ecae0e3274ed2ef11880e74d
+  version: 2026.5.7.17
+  sha256: 5ec0800de5e2ddbd7c663cb4c0c15328f132dc168813897c18866c5c7b93db33
 - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda
   sha256: 7c2df5721c742c2a47b2c8f960e718c930031663ac1174da67c1ed5999f7938c
   md5: edd329d7d3a4ab45dcf905899a7a6115
@@ -9783,9 +10344,9 @@ packages:
   - pkg:pypi/uri-template?source=hash-mapping
   size: 23990
   timestamp: 1733323714454
-- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.6.3-pyhd8ed1ab_0.conda
-  sha256: af641ca7ab0c64525a96fd9ad3081b0f5bcf5d1cbb091afb3f6ed5a9eee6111a
-  md5: 9272daa869e03efe68833e3dc7a02130
+- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.7.0-pyhd8ed1ab_0.conda
+  sha256: feff959a816f7988a0893201aa9727bbb7ee1e9cec2c4f0428269b489eb93fb4
+  md5: cbb88288f74dbe6ada1c6c7d0a97223e
   depends:
   - backports.zstd >=1.0.0
   - brotli-python >=1.2.0
@@ -9796,8 +10357,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/urllib3?source=hash-mapping
-  size: 103172
-  timestamp: 1767817860341
+  size: 103560
+  timestamp: 1778188657149
 - pypi: https://files.pythonhosted.org/packages/b7/ee/e9c95cda829131f71a8dff5ce0406059fd16e591c074414e31ada19ba7c3/validate_pyproject-0.25-py3-none-any.whl
   name: validate-pyproject
   version: '0.25'
@@ -9865,30 +10426,17 @@ packages:
   - mypy ; extra == 'test'
   - pretend ; extra == 'test'
   - pytest ; extra == 'test'
-- pypi: https://files.pythonhosted.org/packages/c6/59/7d02447a55b2e55755011a647479041bc92a82e143f96a8195cb33bd0a1c/virtualenv-21.2.0-py3-none-any.whl
-  name: virtualenv
-  version: 21.2.0
-  sha256: 1bd755b504931164a5a496d217c014d098426cddc79363ad66ac78125f9d908f
-  requires_dist:
-  - distlib>=0.3.7,<1
-  - filelock>=3.24.2,<4 ; python_full_version >= '3.10'
-  - filelock>=3.16.1,<=3.19.1 ; python_full_version < '3.10'
-  - importlib-metadata>=6.6 ; python_full_version < '3.8'
-  - platformdirs>=3.9.1,<5
-  - python-discovery>=1
-  - typing-extensions>=4.13.2 ; python_full_version < '3.11'
-  requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/20/0e/f083a76cb590e60dff3868779558eefefb8dfb7c9ed020babc7aa014ccbf/virtualenv-21.2.1-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/b1/4f/f71e641e504111a5a74e3a20bc52d01bd86788b22699dd3fee1c63253cf6/virtualenv-21.3.1-py3-none-any.whl
   name: virtualenv
-  version: 21.2.1
-  sha256: bd16b49c53562b28cf1a3ad2f36edb805ad71301dee70ddc449e5c88a9f919a2
+  version: 21.3.1
+  sha256: d1a71cf58f2f9228fff23a1f6ec15d39785c6b32e03658d104974247145edd35
   requires_dist:
   - distlib>=0.3.7,<1
   - filelock>=3.24.2,<4 ; python_full_version >= '3.10'
   - filelock>=3.16.1,<=3.19.1 ; python_full_version < '3.10'
   - importlib-metadata>=6.6 ; python_full_version < '3.8'
   - platformdirs>=3.9.1,<5
-  - python-discovery>=1
+  - python-discovery>=1.2.2
   - typing-extensions>=4.13.2 ; python_full_version < '3.11'
   requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/5b/7e/8f322f5e600812e6f9a31b75d242631068ca8f4ef0582dd3ae6e72daecc8/watchdog-6.0.0-cp312-cp312-macosx_11_0_arm64.whl
@@ -9919,17 +10467,17 @@ packages:
   requires_dist:
   - pyyaml>=3.10 ; extra == 'watchmedo'
   requires_python: '>=3.9'
-- conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.6.0-pyhd8ed1ab_0.conda
-  sha256: e298b508b2473c4227206800dfb14c39e4b14fd79d4636132e9e1e4244cdf4aa
-  md5: c3197f8c0d5b955c904616b716aca093
+- conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda
+  sha256: 1ee2d8384972ecbf8630ce8a3ea9d16858358ad3e8566675295e66996d5352da
+  md5: eb9538b8e55069434a18547f43b96059
   depends:
   - python >=3.10
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/wcwidth?source=hash-mapping
-  size: 71550
-  timestamp: 1770634638503
+  size: 82917
+  timestamp: 1777744489106
 - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda
   sha256: 21f6c8a20fe050d09bfda3fb0a9c3493936ce7d6e1b3b5f8b01319ee46d6c6f6
   md5: 6639b6b0d8b5a284f027a2003669aa65
@@ -10124,9 +10672,9 @@ packages:
   purls: []
   size: 265665
   timestamp: 1772476832995
-- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
-  sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
-  md5: 30cd29cb87d819caead4d55184c1d115
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.1-pyhcf101f3_0.conda
+  sha256: 523616c0530d305d2216c2b4a8dfd3872628b60083255b89c5e0d8c42e738cca
+  md5: e1c36c6121a7c9c76f2f148f1e83b983
   depends:
   - python >=3.10
   - python
@@ -10134,8 +10682,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/zipp?source=hash-mapping
-  size: 24194
-  timestamp: 1764460141901
+  size: 24461
+  timestamp: 1776131454755
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
   sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7
   md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829
diff --git a/pixi.toml b/pixi.toml
index c711a178..8614cd57 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -1,27 +1,3 @@
-#######################
-# ENVIRONMENT VARIABLES
-#######################
-
-# Platform-independent
-
-[activation.env]
-PYTHONIOENCODING = 'utf-8'
-
-# Platform-specific
-
-# Ensures the main package is used from the source code during
-# development, even if main package is installed in editable mode
-# via uv. This is important because `pixi update` might replace
-# the installed version with the latest released version.
-
-# Unix/macOS
-[target.unix.activation.env]
-PYTHONPATH = "${PIXI_PROJECT_ROOT}/src${PYTHONPATH:+:${PYTHONPATH}}"
-
-# Windows
-[target.win.activation.env]
-PYTHONPATH = "${PIXI_PROJECT_ROOT}/src;%PYTHONPATH%"
-
 ###########
 # WORKSPACE
 ###########
@@ -32,41 +8,58 @@ PYTHONPATH = "${PIXI_PROJECT_ROOT}/src;%PYTHONPATH%"
 platforms = ['win-64', 'linux-64', 'osx-arm64']
 
 # Channels for fetching packages
-channels = ['conda-forge']
+channels = ['nodefaults', 'conda-forge']
 
-#####################
-# SYSTEM REQUIREMENTS
-#####################
+##########
+# FEATURES
+##########
 
-[system-requirements]
+# Default features:
 
+[activation.env]
+PYTHONIOENCODING = 'utf-8'
+
+[system-requirements]
 # Set minimum supported version for macOS to be 14.0 to ensure packages
-# like `skipp` that only have wheels for macOS 14.0+ (macosx_14_0_arm64)
+# like `scipp` that only have wheels for macOS 14.0+ (macosx_14_0_arm64)
 # are used instead of building from source. This is a workaround for
 # Pixi, see https://github.com/prefix-dev/pixi/issues/5667
 macos = '14.0'
 
-##########
-# FEATURES
-##########
+# Non-default features:
+
+# Set specific Python versions to be used in CI testing.
+
+[feature.py-min.dependencies]
+python = '3.12.*'
+
+[feature.py-max.dependencies]
+python = '3.14.*'
 
-# Default feature configuration
+# Development dependencies for local development and testing with
+# editable installations.
 
-[dependencies]
+[feature.dev.dependencies]
 nodejs = '*'      # Required for Prettier (non-Python formatting)
 jupyterlab = '*'  # Jupyter notebooks
+ipython = '*'     # Interactive Python shell
 pixi-kernel = '*' # Pixi Jupyter kernel
 
-[pypi-dependencies] # == [feature.default.pypi-dependencies]
-#pip = '*' # Native package installer
+[feature.dev.pypi-dependencies]
+pip = '*'
 easydynamics = { path = '.', editable = true, extras = ['dev'] }
 
-# Specific features: Set specific Python versions
+# User-like behavior for testing with pip-installed dependencies instead
+# of editable installations.
 
-[feature.py-min.dependencies]
-python = '3.12.*'
-[feature.py-max.dependencies]
-python = '3.14.*'
+[feature.user.dependencies]
+jupyterlab = '*'  # Jupyter notebooks
+ipython = '*'     # Interactive Python shell
+pixi-kernel = '*' # Pixi Jupyter kernel
+
+[feature.user.pypi-dependencies]
+pip = '*'
+easydynamics = '*'
 
 ##############
 # ENVIRONMENTS
@@ -76,12 +69,18 @@ python = '3.14.*'
 
 # The `default` feature is always included in all environments.
 # Additional features can be specified per environment.
-py-312-env = { features = ['py-min'] }
-py-314-env = { features = ['py-max'] }
 
-# The `default` environment is always created and includes the `default` feature.
-# It does not need to be specified explicitly unless non-default features are included.
-default = { features = ['py-max'] }
+# Specific environments for CI testing with different Python versions.
+py-312-env = { features = ['py-min', 'dev'] }
+py-314-env = { features = ['py-max', 'dev'] }
+
+# The `default` environment is developer-oriented for local development
+# and testing with editable installation of the current package.
+default = { features = ['py-max', 'dev'] }
+
+# The `user` environment allows testing with pip-installed dependencies
+# instead of editable installation of the current package.
+user = { features = ['py-max', 'user'] }
 
 #######
 # TASKS
diff --git a/src/easydynamics/__init__.py b/src/easydynamics/__init__.py
index 54557144..f4c956e5 100644
--- a/src/easydynamics/__init__.py
+++ b/src/easydynamics/__init__.py
@@ -3,11 +3,17 @@
 """EasyDynamics library."""
 
 from easydynamics.analysis import Analysis
+from easydynamics.analysis.fit_binding import FitBinding
+from easydynamics.analysis.parameter_analysis import ParameterAnalysis
 from easydynamics.experiment import Experiment
 from easydynamics.settings.convolution_settings import ConvolutionSettings
+from easydynamics.settings.detailed_balance_settings import DetailedBalanceSettings
 
 __all__ = [
     'Analysis',
     'ConvolutionSettings',
+    'DetailedBalanceSettings',
     'Experiment',
+    'FitBinding',
+    'ParameterAnalysis',
 ]
diff --git a/src/easydynamics/analysis/__init__.py b/src/easydynamics/analysis/__init__.py
index a52854bc..289ec02f 100644
--- a/src/easydynamics/analysis/__init__.py
+++ b/src/easydynamics/analysis/__init__.py
@@ -2,7 +2,9 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from easydynamics.analysis.analysis import Analysis
+from easydynamics.analysis.parameter_analysis import ParameterAnalysis
 
 __all__ = [
     'Analysis',
+    'ParameterAnalysis',
 ]
diff --git a/src/easydynamics/analysis/analysis.py b/src/easydynamics/analysis/analysis.py
index b8493ec9..56b322c7 100644
--- a/src/easydynamics/analysis/analysis.py
+++ b/src/easydynamics/analysis/analysis.py
@@ -285,25 +285,35 @@ def plot_data_and_model(
 
         import plopp as pp
 
+        data_and_model = self.data_and_model_to_datagroup(
+            energy=energy,
+            add_background=add_background,
+            include_components=plot_components,
+        )
+
         plot_kwargs_defaults = {
             'title': self.display_name,
-            'linestyle': {'Data': 'none', 'Model': '-'},
-            'marker': {'Data': 'o', 'Model': None},
-            'color': {'Data': 'black', 'Model': 'red'},
-            'markerfacecolor': {'Data': 'none', 'Model': 'none'},
+            'linestyle': {},
+            'marker': {},
+            'color': {},
+            'markerfacecolor': {},
             'keep': 'energy',
         }
-        data_and_model = {
-            'Data': self.experiment.binned_data,
-            'Model': self._create_model_array(energy=energy),
-        }
 
-        if plot_components:
-            components = self._create_components_dataset(
-                add_background=add_background, energy=energy
-            )
-            for key in components:
-                data_and_model[key] = components[key]
+        for key in data_and_model:
+            if key == 'Data':
+                plot_kwargs_defaults['linestyle'][key] = 'none'
+                plot_kwargs_defaults['marker'][key] = 'o'
+                plot_kwargs_defaults['color'][key] = 'black'
+                plot_kwargs_defaults['markerfacecolor'][key] = 'none'
+
+            elif key == 'Model':
+                plot_kwargs_defaults['linestyle'][key] = '-'
+                plot_kwargs_defaults['marker'][key] = None
+                plot_kwargs_defaults['color'][key] = 'red'
+                plot_kwargs_defaults['markerfacecolor'][key] = 'none'
+
+            else:
                 plot_kwargs_defaults['linestyle'][key] = '--'
                 plot_kwargs_defaults['marker'][key] = None
 
@@ -316,9 +326,79 @@ def plot_data_and_model(
         )
         for widget in fig.bottom_bar[0].controls.values():
             widget.slider_toggler.value = '-o-'
-
+        fig.autoscale()
         return fig
 
+    def data_and_model_to_datagroup(
+        self,
+        energy: sc.Variable | None = None,
+        add_background: bool = True,
+        include_components: bool = True,
+    ) -> sc.DataGroup:
+        """
+        Create a scipp DataGroup containing the experimental data, model calculation and optionally
+        the individual components of the model.
+
+        Parameters
+        ----------
+        energy : sc.Variable | None, default=None
+            The energy values to use for calculating the model. If None, uses the energy from the
+            experiment.
+        add_background : bool, default=True
+            Whether to add background components to the sample model components when creating the
+            DataGroup.
+        include_components : bool, default=True
+            Whether to include the individual components of the model in the DataGroup. If False,
+            only the total model will be included.
+
+        Raises
+        ------
+        ValueError
+            If there is no data to include in the DataGroup, or if there are no Q values available
+            for creating the DataGroup.
+
+        TypeError
+            If add_background is not True or False. If include_components is not True or False.
+
+        Returns
+        -------
+        sc.DataGroup
+            A DataGroup containing the experimental data, model calculation, and optionally the
+            individual components of the model.
+        """
+        if self.experiment.binned_data is None:
+            raise ValueError('No data to include in DataGroup. Please load data first.')
+
+        if self.Q is None:
+            raise ValueError(
+                'No Q values available for creating DataGroup. Please check the experiment data.'
+            )
+
+        if not isinstance(add_background, bool):
+            raise TypeError('add_background must be True or False.')
+
+        if not isinstance(include_components, bool):
+            raise TypeError('include_components must be True or False.')
+
+        energy = self._verify_energy(energy)
+
+        if energy is None:
+            energy = self.energy
+
+        data_and_model = {
+            'Data': self.experiment.binned_data,
+            'Model': self._create_model_array(energy=energy),
+        }
+
+        if include_components:
+            components = self._create_components_dataset(
+                add_background=add_background, energy=energy
+            )
+            for key in components:
+                data_and_model[key] = components[key]
+
+        return sc.DataGroup(data_and_model)
+
     def parameters_to_dataset(self) -> sc.Dataset:
         """
         Creates a scipp dataset with copies of the Parameters in the model.
diff --git a/src/easydynamics/analysis/analysis1d.py b/src/easydynamics/analysis/analysis1d.py
index 489f4381..a97d0681 100644
--- a/src/easydynamics/analysis/analysis1d.py
+++ b/src/easydynamics/analysis/analysis1d.py
@@ -291,11 +291,6 @@ def plot_data_and_model(
         **kwargs : dict[str, Any]
             Keyword arguments to pass to the plotting function.
 
-        Raises
-        ------
-        ValueError
-            If no data is available to plot.
-
         Returns
         -------
         InteractiveFigure
@@ -303,44 +298,121 @@ def plot_data_and_model(
         """
         import plopp as pp
 
+        data_and_model = self.data_and_model_to_datagroup(
+            energy=energy,
+            add_background=add_background,
+            include_components=plot_components,
+        )
+
         plot_kwargs_defaults = {
             'title': self.display_name,
-            'linestyle': {'Data': 'none', 'Model': '-'},
-            'marker': {'Data': 'o', 'Model': 'none'},
-            'color': {'Data': 'black', 'Model': 'red'},
-            'markerfacecolor': {'Data': 'none', 'Model': 'none'},
+            'linestyle': {},
+            'marker': {},
+            'color': {},
+            'markerfacecolor': {},
         }
 
+        for key in data_and_model:
+            if key == 'Data':
+                plot_kwargs_defaults['linestyle'][key] = 'none'
+                plot_kwargs_defaults['marker'][key] = 'o'
+                plot_kwargs_defaults['color'][key] = 'black'
+                plot_kwargs_defaults['markerfacecolor'][key] = 'none'
+
+            elif key == 'Model':
+                plot_kwargs_defaults['linestyle'][key] = '-'
+                plot_kwargs_defaults['marker'][key] = None
+                plot_kwargs_defaults['color'][key] = 'red'
+                plot_kwargs_defaults['markerfacecolor'][key] = 'none'
+
+            else:
+                plot_kwargs_defaults['linestyle'][key] = '--'
+                plot_kwargs_defaults['marker'][key] = None
+
+        # Overwrite defaults with any user-provided kwargs
+        plot_kwargs_defaults.update(kwargs)
+
+        return pp.plot(
+            data_and_model,
+            **plot_kwargs_defaults,
+        )
+
+    def data_and_model_to_datagroup(
+        self,
+        energy: sc.Variable | None = None,
+        add_background: bool = True,
+        include_components: bool = True,
+    ) -> sc.DataGroup:
+        """
+        Create a scipp DataGroup containing the experimental data, model calculation, and
+        optionally the individual components.
+
+        Parameters
+        ----------
+        energy : sc.Variable | None, default=None
+            Optional energy grid to use for the model calculation. If None, the energy grid from
+            the experiment is used.
+        add_background : bool, default=True
+            Whether to add the background to the model prediction when plotting individual
+            components.
+        include_components : bool, default=True
+            Whether to include the individual components of the model in the DataGroup. If True,
+            the DataGroup will include a DataArray for each component with the component's display
+            name as the key
+
+        Raises
+        ------
+        ValueError
+            If no data is available in the experiment to include in the DataGroup. If no Q values
+            are available in the experiment to create the DataGroup. If Q_index is not set to
+            create the DataGroup.
+        TypeError
+            If add_background is not a boolean. If include_components is not a boolean.
+
+        Returns
+        -------
+        sc.DataGroup
+            A DataGroup containing the experimental data, model calculation, and optionally the
+            individual components.
+        """
+
         if self.experiment.binned_data is None:
-            raise ValueError('No data to plot. Please load data first.')
+            raise ValueError('No data to include in DataGroup. Please load data first.')
+
+        if self.Q is None:
+            raise ValueError(
+                'No Q values available for creating DataGroup. Please check the experiment data.'
+            )
+
+        if not isinstance(add_background, bool):
+            raise TypeError('add_background must be True or False.')
+
+        if not isinstance(include_components, bool):
+            raise TypeError('include_components must be True or False.')
+
+        if self.Q_index is None:
+            raise ValueError('Q_index must be set to create DataGroup.')
 
         energy = self._verify_energy(energy)
+
         if energy is None:
             energy = self._masked_energy
 
-        # Create a dataset containing the data, model, and individual
-        # components for plotting.
         data_and_model = {
             'Data': self.experiment.binned_data['Q', self.Q_index],
             'Model': self._create_model_array(energy=energy),
         }
 
-        if plot_components:
+        if include_components:
             components = self._create_components_dataset_single_Q(
-                add_background=add_background, energy=energy
+                add_background=add_background,
+                energy=energy,
             )
-            for comp_name in components:
-                data_and_model[comp_name] = components[comp_name]
-                plot_kwargs_defaults['linestyle'][comp_name] = '--'
-                plot_kwargs_defaults['marker'][comp_name] = None
 
-        # Overwrite defaults with any user-provided kwargs
-        plot_kwargs_defaults.update(kwargs)
+            for key in components:
+                data_and_model[key] = components[key]
 
-        return pp.plot(
-            data_and_model,
-            **plot_kwargs_defaults,
-        )
+        return sc.DataGroup(data_and_model)
 
     def fix_energy_offset(self) -> None:
         """Fix the energy offset parameter for the current Q index."""
@@ -385,30 +457,6 @@ def _on_Q_index_changed(self) -> None:
         self._masked_energy = masked_energy
         self._convolver = self._create_convolver()
 
-    def _verify_energy(self, energy: sc.Variable | None) -> sc.Variable | None:
-        """
-        Verify that the provided energy is the correct type.
-
-        Parameters
-        ----------
-        energy : sc.Variable | None
-            The energy to verify.
-
-        Raises
-        ------
-        TypeError
-            If energy is not a sc.Variable or None.
-
-        Returns
-        -------
-        sc.Variable | None
-            The verified energy, or None if no energy is provided.
-        """
-
-        if energy is not None and not isinstance(energy, sc.Variable):
-            raise TypeError(f'Energy must be a sc.Variable or None. Got {type(energy)}.')
-        return energy
-
     def _calculate_energy_with_offset(
         self,
         energy: sc.Variable,
diff --git a/src/easydynamics/analysis/analysis_base.py b/src/easydynamics/analysis/analysis_base.py
index 01f0a20f..ac30a9ea 100644
--- a/src/easydynamics/analysis/analysis_base.py
+++ b/src/easydynamics/analysis/analysis_base.py
@@ -432,6 +432,64 @@ def normalize_resolution(self) -> None:
         """
         self.instrument_model.normalize_resolution()
 
+    def get_parameters_near_bounds(
+        self,
+        rtol: float = 1e-5,
+        atol: float = 1e-8,
+    ) -> list[Parameter]:
+        """
+        Get a list of parameters that are near their bounds.
+
+        Parameters
+        ----------
+        rtol : float, default=1e-5
+            Relative tolerance for determining if a parameter is near its bound.
+        atol : float, default=1e-8
+            Absolute tolerance for determining if a parameter is near its bound.
+
+        Returns
+        -------
+        list[Parameter]
+            A list of parameters that are near their bounds.
+
+        Raises
+        ------
+        TypeError
+            If rtol or atol is not a float.
+        ValueError
+            If rtol or atol is negative.
+        """
+
+        if not isinstance(rtol, (int, float)):
+            raise TypeError(f'rtol must be a float. Got {type(rtol)}.')
+
+        if rtol < 0:
+            raise ValueError(f'rtol must be non-negative. Got {rtol}.')
+
+        if not isinstance(atol, (int, float)):
+            raise TypeError(f'atol must be a float. Got {type(atol)}.')
+
+        if atol < 0:
+            raise ValueError(f'atol must be non-negative. Got {atol}.')
+
+        parameters = self.get_all_parameters()
+        at_bounds = []
+
+        for p in parameters:
+            value = p.value
+            if not np.isfinite(value):
+                at_bounds.append(p)
+                continue
+
+            at_min = not np.isneginf(p.min) and np.isclose(value, p.min, rtol=rtol, atol=atol)
+
+            at_max = not np.isposinf(p.max) and np.isclose(value, p.max, rtol=rtol, atol=atol)
+
+            if at_min or at_max:
+                at_bounds.append(p)
+
+        return at_bounds
+
     #############
     # Private methods
     #############
@@ -491,6 +549,30 @@ def _verify_Q_index(self, Q_index: int | None) -> int | None:
             raise IndexError('Q_index must be a valid index for the Q values.')
         return Q_index
 
+    def _verify_energy(self, energy: sc.Variable | None) -> sc.Variable | None:
+        """
+        Verify that the provided energy is the correct type.
+
+        Parameters
+        ----------
+        energy : sc.Variable | None
+            The energy to verify.
+
+        Raises
+        ------
+        TypeError
+            If energy is not a sc.Variable or None.
+
+        Returns
+        -------
+        sc.Variable | None
+            The verified energy, or None if no energy is provided.
+        """
+
+        if energy is not None and not isinstance(energy, sc.Variable):
+            raise TypeError(f'Energy must be a sc.Variable or None. Got {type(energy)}.')
+        return energy
+
     #############
     # Dunder methods
     #############
diff --git a/src/easydynamics/analysis/fit_binding.py b/src/easydynamics/analysis/fit_binding.py
new file mode 100644
index 00000000..ad11ff60
--- /dev/null
+++ b/src/easydynamics/analysis/fit_binding.py
@@ -0,0 +1,327 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from easydynamics.base_classes.easydynamics_base import EasyDynamicsBase
+from easydynamics.sample_model.component_collection import ComponentCollection
+from easydynamics.sample_model.components.model_component import ModelComponent
+from easydynamics.sample_model.diffusion_model.diffusion_model_base import DiffusionModelBase
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+class FitBinding(EasyDynamicsBase):
+    """
+    Contract between dataset, model, and fit function for ParameterAnalysis. This class
+    encapsulates the necessary information to bind a dataset key to a model and convert it into a
+    fit function callable.
+    """
+
+    def __init__(
+        self,
+        parameter_name: str,
+        model: ModelComponent | ComponentCollection | DiffusionModelBase,
+        modes: str | list[str] | None = None,
+        display_name: str | None = None,
+        unique_name: str | None = None,
+    ) -> None:
+        """
+        Initialize a FitBinding.
+
+        Parameters
+        ----------
+        parameter_name : str
+            The name of the parameter to fit. This should correspond to a key in the dataset.
+        model : ModelComponent | ComponentCollection | DiffusionModelBase
+            The model to fit. This can be a single ModelComponent, a ComponentCollection, or a
+            DiffusionModelBase.
+        modes : str | list[str] | None, default=None
+            The modes to fit for diffusion models. This can be a single string, a list of strings,
+            or None (which defaults to ["area", "width"]). Only applicable if the model is a
+            DiffusionModelBase. Default is None.
+        display_name : str | None, default=None
+            An optional display name for the FitBinding. If None, the unique_name will be used.
+            Default is None.
+        unique_name : str | None, default=None
+            An optional unique name for the FitBinding. If None, a unique name will be generated.
+            Default is None.
+
+        Raises
+        ------
+        TypeError
+            If parameter_name is not a string, if model is not a ModelComponent,
+            ComponentCollection or DiffusionModelBase, or if modes is not a string, list of
+            strings, or None.
+
+        Examples
+        --------
+        1. Basic usage with a ModelComponent:
+        >>> import easydynamics.sample_model as sm
+        >>> import easydynamics as edyn
+        >>> fit_func = sm.Polynomial(coefficients=[3.7, -0.5], display_name='Straight line')
+        >>> binding = edyn.FitBinding(parameter_name='Gaussian area', model=fit_func)
+        >>> print(binding)
+        FitBinding(parameter_name='Gaussian area', model=Polynomial(unique_name = Polynomial_1,
+        unit = meV, coefficients = [Straight line_c0=3.7, Straight line_c1=-0.5]), modes=None)
+
+        2. Usage with a DiffusionModelBase and specific modes:
+        >>> brownian_diffusion_model = sm.BrownianTranslationalDiffusion(
+        ...     display_name='Brownian Translational Diffusion',
+        ...     diffusion_coefficient=2.4e-9,
+        ...     scale=0.5,
+        ... )
+        >>> binding = edyn.FitBinding(
+        ...     parameter_name='Lorentzian',
+        ...     model=brownian_diffusion_model,
+        ...     modes=['area', 'width'],
+        ... )
+        FitBinding(parameter_name=Lorentzian, model=Brownian Translational Diffusion,
+        modes=['area', 'width'], display_name=FitBinding_1, unique_name=FitBinding_1)
+        """
+
+        super().__init__(display_name=display_name, unique_name=unique_name)
+
+        if not isinstance(parameter_name, str):
+            raise TypeError('parameter_name must be a string')
+
+        if not isinstance(model, (ModelComponent, ComponentCollection, DiffusionModelBase)):
+            raise TypeError(
+                'model must be a ModelComponent, ComponentCollection, or DiffusionModelBase'
+            )
+
+        if modes is not None and not isinstance(modes, (str, list)):
+            raise TypeError('modes must be a string, list of strings, or None')
+
+        if isinstance(modes, list) and not all(isinstance(mode, str) for mode in modes):
+            raise TypeError('All modes in the list must be strings')
+
+        self._parameter_name = parameter_name
+        self._model = model
+        self._modes = modes
+
+    # ------------------------------------------------------------------
+    # Properties
+    # ------------------------------------------------------------------
+
+    @property
+    def parameter_name(self) -> str:
+        """
+        The name of the parameter to fit. This should correspond to a key in the dataset.
+
+        Returns
+        -------
+        str
+            The name of the parameter to fit.
+        """
+        return self._parameter_name
+
+    @parameter_name.setter
+    def parameter_name(self, value: str) -> None:
+        """
+        Set the name of the parameter to fit.
+
+        Parameters
+        ----------
+        value : str
+            The new name of the parameter to fit.
+
+        Raises
+        ------
+        TypeError
+            If the value is not a string.
+        """
+        if not isinstance(value, str):
+            raise TypeError('parameter_name must be a string')
+        self._parameter_name = value
+
+    @property
+    def model(self) -> ModelComponent | ComponentCollection | DiffusionModelBase:
+        """
+        The model to fit. This can be a single ModelComponent, a ComponentCollection, or a
+        DiffusionModelBase.
+
+        Returns
+        -------
+        ModelComponent | ComponentCollection | DiffusionModelBase
+            The model to fit.
+        """
+        return self._model
+
+    @model.setter
+    def model(self, value: ModelComponent | ComponentCollection | DiffusionModelBase) -> None:
+        """
+        Set the model to fit.
+
+        Parameters
+        ----------
+        value : ModelComponent | ComponentCollection | DiffusionModelBase
+            The new model to fit.
+
+        Raises
+        ------
+        TypeError
+            If the value is not a ModelComponent, ComponentCollection, or DiffusionModelBase.
+        """
+        if not isinstance(value, (ModelComponent, ComponentCollection, DiffusionModelBase)):
+            raise TypeError(
+                'model must be a ModelComponent, ComponentCollection, or DiffusionModelBase.'
+            )
+        self._model = value
+
+    @property
+    def modes(self) -> str | list[str] | None:
+        """
+        The modes to fit for diffusion models. This can be a single string, a list of strings, or
+        None (which defaults to ["area", "width"]).
+
+        Returns
+        -------
+        str | list[str] | None
+            The modes to fit for diffusion models.
+        """
+        return self._modes
+
+    @modes.setter
+    def modes(self, value: str | list[str] | None) -> None:
+        """
+        Set the modes to fit for diffusion models.
+
+        Parameters
+        ----------
+        value : str | list[str] | None
+            The new modes to fit for diffusion models.
+
+        Raises
+        ------
+        TypeError
+            If the value is not a string, list of strings, or None.
+        """
+        if value is not None and not isinstance(value, (str, list)):
+            raise TypeError('modes must be a string, list of strings, or None')
+
+        if isinstance(value, str):
+            value = [value]
+        if isinstance(value, list) and not all(isinstance(mode, str) for mode in value):
+            raise TypeError('All modes in the list must be strings')
+        self._modes = value
+
+    # ------------------------------------------------------------------
+    # Other methods
+    # ------------------------------------------------------------------
+
+    def build_callables(self) -> list[Callable]:
+        """
+        Build the callables for fitting based on the model and modes.
+
+        Returns
+        -------
+        list[Callable]
+            A list of callables for fitting.
+        """
+        modes = self._get_modes()
+
+        if isinstance(self.model, DiffusionModelBase):
+            return [self._build_diffusion_callable(mode) for mode in modes]
+
+        return [lambda x, **_: self.model.evaluate(x)]
+
+    def get_model_names(self) -> list[str]:
+        """
+        Get the names of the models based on the current modes.
+
+        Returns
+        -------
+        list[str]
+            A list of model names.
+        """
+        modes = self._get_modes()
+
+        if isinstance(self.model, DiffusionModelBase):
+            return [f'{self.model.display_name} {mode}' for mode in modes]
+
+        return [self.model.display_name]
+
+    def get_parameter_names(self) -> list[str]:
+        """
+        Get the names of the parameters based on the current modes.
+
+        Returns
+        -------
+        list[str]
+            A list of parameter names.
+        """
+        modes = self._get_modes()
+
+        if isinstance(self.model, DiffusionModelBase):
+            return [f'{self.parameter_name} {mode}' for mode in modes]
+
+        return [self.parameter_name]
+
+    # ------------------------------------------------------------------
+    # Private methods
+    # ------------------------------------------------------------------
+
+    def _build_diffusion_callable(self, mode: str) -> Callable:
+        """
+        Build a callable for a specific diffusion mode.
+
+        Parameters
+        ----------
+        mode : str
+            The diffusion mode ("area" or "width").
+
+        Returns
+        -------
+        Callable
+            A callable for the specified diffusion mode.
+
+        Raises
+        ------
+        ValueError
+            If the mode is unknown.
+        """
+        model = self.model
+
+        if mode == 'area':
+            return lambda x, **_: model.calculate_QISF(x) * model.scale.value
+
+        if mode == 'width':
+            return lambda x, **_: model.calculate_width(x)
+
+        raise ValueError(f'Unknown diffusion mode: {mode}')
+
+    def _get_modes(self) -> list[str]:
+        """
+        Get the modes to fit for diffusion models, defaulting to ["area", "width"] if not set.
+
+        Returns
+        -------
+        list[str]
+            The modes to fit for diffusion models.
+        """
+        return ['area', 'width'] if self.modes is None else self.modes
+
+    # ------------------------------------------------------------------
+    # dunder methods
+    # ------------------------------------------------------------------
+
+    def __repr__(self) -> str:
+        """
+        Return a string representation of the FitBinding.
+
+        Returns
+        -------
+        str
+            A string representation of the FitBinding.
+        """
+        return (
+            f'FitBinding(parameter_name={self.parameter_name},\n '
+            f'model={self.model.display_name},\n '
+            f'modes={self.modes},\n '
+            f'display_name={self.display_name},\n '
+            f'unique_name={self.unique_name})'
+        )
diff --git a/src/easydynamics/analysis/parameter_analysis.py b/src/easydynamics/analysis/parameter_analysis.py
new file mode 100644
index 00000000..eb3e3b4f
--- /dev/null
+++ b/src/easydynamics/analysis/parameter_analysis.py
@@ -0,0 +1,557 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+import itertools
+from typing import Any
+
+import numpy as np
+import plopp as pp
+import scipp as sc
+from easyscience.fitting.minimizers.utils import FitResults
+from easyscience.fitting.multi_fitter import MultiFitter
+from matplotlib import rcParams
+from plopp.backends.matplotlib.figure import InteractiveFigure
+
+from easydynamics.analysis.analysis import Analysis
+from easydynamics.analysis.fit_binding import FitBinding
+from easydynamics.base_classes.easydynamics_modelbase import EasyDynamicsModelBase
+from easydynamics.utils.utils import _in_notebook
+
+
+class ParameterAnalysis(EasyDynamicsModelBase):
+    """
+    For analysing fitted parameters.
+
+    Can be used to fit parameters to ModelComponents, ComponentCollections, or DiffusionModelBase
+    objects, and to plot the parameters and fit results. The parameters to be analyzed can be
+    provided as a sc.Dataset or directly as an Analysis object. Multiple parameters can be fitted
+    simultaneously, and the fit functions can be customized for each parameter. For diffusion
+    models, the area and width can be fitted separately (or not at all) by specifying fit settings.
+    """
+
+    def __init__(
+        self,
+        parameters: sc.Dataset | Analysis | None = None,
+        bindings: FitBinding | list[FitBinding] | None = None,
+        display_name: str | None = 'ParameterAnalysis',
+        unique_name: str | None = None,
+    ) -> None:
+        """
+        Initialize the ParameterAnalysis.
+
+        Parameters
+        ----------
+        parameters : sc.Dataset | Analysis | None, default=None
+            The parameters to analyze. Can be provided as a sc.Dataset or as an Analysis (in which
+            case the parameters will be extracted from the Analysis).
+        bindings : FitBinding | list[FitBinding] | None, default=None
+            The fit bindings to use for fitting the parameters. Can be a single FitBinding or a
+            list of FitBindings. If None, no fit bindings are provided.
+        display_name : str | None, default='ParameterAnalysis'
+            Display name of the analysis.
+        unique_name : str | None, default=None
+            Unique name of the analysis. If None, a unique name is automatically generated. By
+            default, None.
+        """
+
+        super().__init__(display_name=display_name, unique_name=unique_name)
+
+        self._parameters = self._verify_parameters(parameters)
+
+        self._bindings = self._verify_bindings(bindings)
+
+    #############
+    # Properties
+    #############
+    @property
+    def parameters(self) -> sc.Dataset | None:
+        """
+        Get the parameters for the parameter analysis.
+
+        Returns
+        -------
+        sc.Dataset | None
+            The parameters for the parameter analysis.
+        """
+        return self._parameters
+
+    @parameters.setter
+    def parameters(self, value: sc.Dataset | Analysis | None) -> None:
+        """
+        Set the parameter dataset for the parameter analysis.
+
+        Parameters
+        ----------
+        value : sc.Dataset | Analysis | None
+            The new parameter dataset for the parameter analysis.
+        """
+        self._parameters = self._verify_parameters(value)
+
+    @property
+    def bindings(self) -> list[FitBinding]:
+        """
+        Get the fit bindings for the parameter analysis.
+
+        Returns
+        -------
+        list[FitBinding]
+            The fit bindings for the parameter analysis.
+        """
+        return self._bindings
+
+    @bindings.setter
+    def bindings(self, value: FitBinding | list[FitBinding] | None) -> None:
+        """
+        Set the fit bindings for the parameter analysis.
+
+        Parameters
+        ----------
+        value : FitBinding | list[FitBinding] | None
+            The new fit bindings for the parameter analysis.
+        """
+        self._bindings = self._verify_bindings(value)
+
+    #############
+    # Other methods
+    #############
+
+    def fit(self) -> FitResults:
+        """
+        Fit the parameters using the specified fit functions and settings.
+
+        Returns
+        -------
+        FitResults
+            The results of the fit
+
+        Raises
+        ------
+        ValueError
+            If no parameters Dataset is provided. If no fit functions are provided. If no parameter
+            names are found for the fit functions.
+        """
+
+        if self.parameters is None:
+            raise ValueError('No parameters Dataset provided.')
+
+        if not self.bindings:
+            raise ValueError('No fit bindings provided.')
+
+        xs = []
+        ys = []
+        ws = []
+        funcs, models = [], []
+
+        for binding in self.bindings:
+            param_names = binding.get_parameter_names()
+            callables = binding.build_callables()
+
+            for pname, func in zip(param_names, callables, strict=True):
+                if pname not in self.parameters:
+                    raise ValueError(
+                        f"Parameter '{pname}' from binding '{binding.unique_name}' "
+                        f'not found in parameters Dataset.'
+                    )
+
+                x, y, weight = self._get_xyweight_from_dataset(pname)
+
+                xs.append(x)
+                ys.append(y)
+                ws.append(weight)
+
+                funcs.append(func)
+                models.append(binding.model)
+
+        mf = MultiFitter(
+            fit_objects=models,
+            fit_functions=funcs,
+        )
+
+        return mf.fit(
+            x=xs,
+            y=ys,
+            weights=ws,
+        )
+
+    def plot(
+        self, names: str | list[str] | None = None, **kwargs: dict[str, Any]
+    ) -> InteractiveFigure:
+        """
+        Plot the parameters and fit results.
+
+        Parameters
+        ----------
+        names : str | list[str] | None, default=None
+            The names of the parameters to plot. If None, all parameters with bindings are plotted.
+        **kwargs : dict[str, Any]
+            Additional keyword arguments to pass to the plotting function.
+
+        Returns
+        -------
+        InteractiveFigure
+            An interactive figure containing the plots of the parameters and fit results.
+
+        Raises
+        ------
+        ValueError
+            If the units of the specified parameters are not consistent.
+        RuntimeError
+            If plot() is called outside of a Jupyter notebook environment.
+        """
+
+        if not _in_notebook():
+            raise RuntimeError('plot() can only be used in a Jupyter notebook environment.')
+
+        if self.parameters is None:
+            raise ValueError('No parameters available to plot.')
+
+        full_model_dataset = None
+        if self.bindings:
+            full_model_dataset = self.calculate_model_dataset(self.bindings)
+
+        # If no names are provided, default to plot all parameters that have bindings.
+        # If no bindings are provided, plot all parameters.
+        if names is None:
+            names = []
+
+            if not self.bindings:
+                names = list(self.parameters.keys())
+            else:
+                for b in self.bindings:
+                    names.extend(b.get_parameter_names())
+
+        names = self._normalize_names(names)
+
+        # Check that the units of the specified parameters are consistent.
+        units = [self.parameters[name].unit for name in names]
+        first_unit = units[0]
+        if any(unit != first_unit for unit in units):
+            raise ValueError(f'Units are not compatible, and cannot be plotted together: {units}')
+
+        color_cycle = itertools.cycle(rcParams['axes.prop_cycle'].by_key()['color'])
+        markers = itertools.cycle(['o', 's', 'D', '^', 'v', '<', '>'])
+
+        plot_kwargs = {
+            'title': self.display_name,
+            'linestyle': {},
+            'marker': {},
+            'color': {},
+            'markerfacecolor': {},
+        }
+
+        data_arrays = {}
+        model_arrays = {}
+
+        # map parameter names to model names
+        param_to_model = {}
+        if self.bindings is not None:
+            for b in self.bindings:
+                param_names = b.get_parameter_names()
+                model_names = b.get_model_names()
+
+                param_to_model.update(dict(zip(param_names, model_names, strict=True)))
+
+        for pname in names:
+            data_arrays[pname] = self.parameters[pname]
+            color = next(color_cycle)
+            marker = next(markers)
+
+            # Data styling
+            plot_kwargs['linestyle'][pname] = 'none'
+            plot_kwargs['marker'][pname] = marker
+            plot_kwargs['color'][pname] = color
+            plot_kwargs['markerfacecolor'][pname] = 'none'
+
+            if full_model_dataset is not None and pname in param_to_model:
+                mname = param_to_model[pname]
+                model_arrays[mname] = full_model_dataset[mname]
+
+                # Model styling
+                plot_kwargs['linestyle'][mname] = '--'
+                plot_kwargs['marker'][mname] = None
+                plot_kwargs['color'][mname] = color
+
+        # Update kwargs with user provided kwargs.
+        plot_kwargs.update(kwargs)
+
+        data_and_model = sc.Dataset(data_arrays)
+        data_and_model.update(model_arrays)
+
+        return pp.plot(data_and_model, **plot_kwargs)
+
+    def calculate_model_dataset(self, bindings: list[FitBinding]) -> sc.Dataset:
+        """
+        Evaluate all bindings into a sc.Dataset of model predictions.
+
+        Parameters
+        ----------
+        bindings : list[FitBinding]
+            The bindings to evaluate.
+
+        Returns
+        -------
+        sc.Dataset
+            A sc.Dataset containing the model predictions for all bindings.
+
+        Raises
+        ------
+        ValueError
+            If any parameter name from the bindings is not found in the parameters Dataset.
+
+        TypeError
+            If bindings is not a list of FitBinding objects.
+        """
+
+        if self.parameters is None:
+            raise ValueError('No parameters Dataset provided.')
+
+        if not bindings:
+            raise ValueError('No fit bindings provided.')
+
+        if not isinstance(bindings, list) or not all(isinstance(b, FitBinding) for b in bindings):
+            raise TypeError('bindings must be a list of FitBinding objects.')
+
+        arrays = {}
+
+        for b in bindings:
+            param_names = b.get_parameter_names()
+            model_names = b.get_model_names()
+            callables = b.build_callables()
+
+            for pname, mname, func in zip(param_names, model_names, callables, strict=True):
+                if pname not in self.parameters:
+                    raise ValueError(
+                        f"Parameter '{pname}' from binding '{b.unique_name}' "
+                        f'not found in parameters Dataset.'
+                    )
+                da = self.parameters[pname]
+                x = da.coords['Q']
+
+                y_model = func(x.values)
+
+                arrays[mname] = sc.DataArray(
+                    data=sc.array(dims=['Q'], values=y_model, unit=da.unit),
+                    coords={'Q': x},
+                )
+        return sc.Dataset(arrays)
+
+    def append_binding(self, binding: FitBinding) -> None:
+        """
+        Append a FitBinding to the list of bindings for the parameter analysis.
+
+        Parameters
+        ----------
+        binding : FitBinding
+            The FitBinding to append.
+
+        Raises
+        ------
+        TypeError
+            If binding is not a FitBinding object.
+        """
+        if not isinstance(binding, FitBinding):
+            raise TypeError('binding must be a FitBinding object.')
+        self._bindings.append(binding)
+
+    def clear_bindings(self) -> None:
+        """
+        Clear all FitBindings from the list of bindings for the parameter analysis.
+        """
+        self._bindings.clear()
+
+    def get_all_variables(self) -> list:
+        """
+        Get all variables from the fit functions.
+
+        Returns
+        -------
+        list
+            A list of all variables from the fit functions.
+        """
+        variables = set()
+        for b in self._bindings:
+            variables.update(b.model.get_all_variables())
+        return list(variables)
+
+    #############
+    # Private methods: verification and preparation
+    #############
+
+    def _verify_bindings(self, bindings: FitBinding | list[FitBinding] | None) -> list[FitBinding]:
+        """
+        Verify the bindings input.
+
+        Parameters
+        ----------
+        bindings : FitBinding | list[FitBinding] | None
+            The bindings to verify.
+
+        Returns
+        -------
+        list[FitBinding]
+            A list of verified FitBindings.
+
+        Raises
+        ------
+        TypeError
+            If bindings is not a FitBinding, a list of FitBindings, or None.
+        """
+        if bindings is None:
+            return []
+        if isinstance(bindings, FitBinding):
+            return [bindings]
+        if isinstance(bindings, list) and all(isinstance(b, FitBinding) for b in bindings):
+            return bindings
+        raise TypeError('bindings must be a FitBinding, a list of FitBindings, or None.')
+
+    def _verify_parameters(self, parameters: sc.Dataset | Analysis | None) -> sc.Dataset | None:
+        """
+        Verify the parameters input and convert it to a sc.Dataset if it's an Analysis.
+
+        Parameters
+        ----------
+        parameters : sc.Dataset | Analysis | None
+            The parameters to verify.
+
+        Returns
+        -------
+        sc.Dataset | None
+            The verified parameters as a sc.Dataset, or None if no parameters were provided.
+
+        Raises
+        ------
+        TypeError
+            If parameters is not a sc.Dataset, an Analysis, or None.
+        ValueError
+            If parameters is a sc.Dataset but does not have a 'Q' coordinate.
+        """
+        if parameters is None:
+            return None
+
+        if not isinstance(parameters, (sc.Dataset, Analysis)):
+            raise TypeError(r'parameters must be a sc.Dataset, an Analysis, or None.')
+
+        if isinstance(parameters, Analysis):
+            verified_parameters = parameters.parameters_to_dataset()
+        else:
+            verified_parameters = parameters
+
+        if 'Q' not in verified_parameters.coords:
+            raise ValueError(r"parameters must have a 'Q' coordinate.")
+        return verified_parameters
+
+    def _normalize_names(self, names: str | list[str] | None) -> list[str] | None:
+        """
+        Normalize the names input to a list of strings and verify that they exist in the parameters
+        Dataset.
+
+        Parameters
+        ----------
+        names : str | list[str] | None
+            The names to normalize and verify.
+
+        Returns
+        -------
+        list[str] | None
+            The normalized list of names, or None if names was None.
+
+        Raises
+        ------
+        ValueError
+            If any of the specified names are not found in the parameters Dataset, or if names is a
+            list that contains non-string elements.
+        """
+        if names is None:
+            return None
+        if not isinstance(names, (str, list)):
+            raise ValueError('names must be a string, a list of strings, or None.')
+        if isinstance(names, list):
+            if not all(isinstance(name, str) for name in names):
+                raise ValueError('All names in the list must be strings.')
+            for name in names:
+                if name not in self.parameters:
+                    raise ValueError(f"Parameter name '{name}' not found in parameters Dataset.")
+        if isinstance(names, str):
+            if names not in self.parameters:
+                raise ValueError(f"Parameter name '{names}' not found in parameters Dataset.")
+            names = [names]
+        return names
+
+    #############
+    # Private methods
+    #############
+
+    def _get_xyweight_from_dataset(
+        self, parameter_name: str
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Get the x, y, and weight values for a given parameter name from the parameters Dataset.
+
+        Parameters
+        ----------
+        parameter_name : str
+            The name of the parameter to get x, y, and weight values for.
+
+        Returns
+        -------
+        tuple[np.ndarray, np.ndarray, np.ndarray]
+            A tuple containing the x, y, and weight values for the given parameter name.
+
+        Raises
+        ------
+        ValueError
+            If the parameter name is not found in the parameters Dataset. If non-finite weights are
+            found for the parameter.
+        """
+        if self._parameters is None:
+            raise ValueError('No parameters Dataset provided.')
+        if parameter_name not in self._parameters:
+            raise ValueError(f"Parameter name '{parameter_name}' not found in parameters Dataset.")
+
+        variances = self._parameters[parameter_name].variances
+        if variances is None:
+            weight = np.ones_like(self._parameters[parameter_name].values)
+        elif np.any(~np.isfinite(variances)) or np.any(variances <= 0):
+            raise ValueError(
+                f"Non-finite variances found for parameter '{parameter_name}', "
+                f'cannot compute weights.'
+            )
+        else:
+            weight = 1 / np.sqrt(variances)
+
+        return (
+            self._parameters[parameter_name].coords['Q'].values,
+            self._parameters[parameter_name].values,
+            weight,
+        )
+
+    #############
+    # Dunder methods
+    #############
+    def __repr__(self) -> str:
+        cls = self.__class__.__name__
+
+        n_params = len(self._parameters) if isinstance(self._parameters, sc.Dataset) else 0
+
+        param_names = (
+            list(self._parameters.keys()) if isinstance(self._parameters, sc.Dataset) else None
+        )
+
+        binding_info = [
+            {
+                'parameter': b.parameter_name,
+                'model': b.model.display_name,
+                'modes': b.modes,
+            }
+            for b in self._bindings
+        ]
+
+        return (
+            f'{cls}(\n'
+            f'display_name={self.display_name},\n'
+            f'unique_name={self.unique_name},\n'
+            f'n_parameters={n_params},\n'
+            f'parameter_names={param_names},\n'
+            f'bindings={binding_info}\n'
+            f')'
+        )
diff --git a/src/easydynamics/convolution/numerical_convolution.py b/src/easydynamics/convolution/numerical_convolution.py
index 7a990d37..d39550cb 100644
--- a/src/easydynamics/convolution/numerical_convolution.py
+++ b/src/easydynamics/convolution/numerical_convolution.py
@@ -98,14 +98,15 @@ def convolution(
             self._energy_grid = self._create_energy_grid()
 
         # Give warnings if peaks are very wide or very narrow
-        self._check_width_thresholds(
-            model=self.sample_components,
-            model_name='sample model',
-        )
-        self._check_width_thresholds(
-            model=self.resolution_components,
-            model_name='resolution model',
-        )
+        if not self.convolution_settings.suppress_warnings:
+            self._check_width_thresholds(
+                model=self.sample_components,
+                model_name='sample model',
+            )
+            self._check_width_thresholds(
+                model=self.resolution_components,
+                model_name='resolution model',
+            )
 
         # Evaluate sample model. If called via the Convolution class,
         # delta functions are already filtered out.
diff --git a/src/easydynamics/experiment/experiment.py b/src/easydynamics/experiment/experiment.py
index 909fb35a..25b28cef 100644
--- a/src/easydynamics/experiment/experiment.py
+++ b/src/easydynamics/experiment/experiment.py
@@ -437,6 +437,7 @@ def plot_data(
             )
             for widget in fig.bottom_bar[0].controls.values():
                 widget.slider_toggler.value = '-o-'
+            fig.autoscale()
 
         else:
             if transpose_axes:
diff --git a/src/easydynamics/sample_model/components/expression_component.py b/src/easydynamics/sample_model/components/expression_component.py
index 79ce73ef..e51de905 100644
--- a/src/easydynamics/sample_model/components/expression_component.py
+++ b/src/easydynamics/sample_model/components/expression_component.py
@@ -8,6 +8,7 @@
 
 import sympy as sp
 from easyscience.variable import Parameter
+from scipy.special import erf
 
 from easydynamics.sample_model.components.model_component import ModelComponent
 from easydynamics.utils.utils import Numeric
@@ -20,11 +21,6 @@
 class ExpressionComponent(ModelComponent):
     """
     Model component defined by a symbolic expression.
-
-    Example: expr = ExpressionComponent( "A * exp(-(x - x0)**2 / (2*sigma**2))", parameters={"A":
-    10, "x0": 0, "sigma": 1}, )
-
-        expr.A = 5 y = expr.evaluate(x)
     """
 
     # -------------------------
@@ -100,6 +96,18 @@ def __init__(
             If the expression is invalid or does not contain 'x'.
         TypeError
             If any parameter value is not numeric.
+
+        Examples
+        --------
+        >>> expr = ExpressionComponent(
+        ...     'A * exp(-(x - x0)**2 / (2*sigma**2))',
+        ...     parameters={'A': 10, 'x0': 0, 'sigma': 1},
+        ...     unit='meV',
+        ...     display_name='Gaussian Peak',
+        ... )
+
+        >>> expr.A = 5
+        >>> y = expr.evaluate(x)
         """
         super().__init__(unit=unit, display_name=display_name, unique_name=unique_name)
 
@@ -157,8 +165,11 @@ def __init__(
 
         if parameters is not None:
             for name, value in parameters.items():
-                if not isinstance(value, Numeric):
-                    raise TypeError(f"Parameter '{name}' must be numeric")
+                if not isinstance(value, (Numeric, Parameter, dict)):
+                    raise TypeError(
+                        f"Parameter '{name}' must be numeric, "
+                        f'a Parameter instance, or a dictionary, got {type(value).__name__}'
+                    )
         parameters = parameters or {}
         self._parameters: dict[str, Parameter] = {}
 
@@ -168,12 +179,17 @@ def __init__(
                 continue
 
             value = parameters.get(name, 1.0)
+            if isinstance(value, Parameter):
+                self._parameters[name] = value
 
-            self._parameters[name] = Parameter(
-                name=name,
-                value=value,
-                unit=self._unit,
-            )
+            elif isinstance(value, dict) and value.get('@class') == 'Parameter':
+                self._parameters[name] = Parameter.from_dict(value)
+            else:
+                self._parameters[name] = Parameter(
+                    name=name,
+                    value=value,
+                    unit=self._unit,
+                )
 
         # Create numerical function
         ordered_symbols = [sp.Symbol(name) for name in self._symbol_names]
@@ -181,7 +197,7 @@ def __init__(
         self._func = sp.lambdify(
             ordered_symbols,
             self._expr,
-            modules=['numpy'],
+            modules=[{'erf': erf}, 'numpy'],
         )
 
         # -------------------------
@@ -190,7 +206,14 @@ def __init__(
 
     @property
     def expression(self) -> str:
-        """Return the original expression string."""
+        """
+        Return the original expression string.
+
+        Returns
+        -------
+        str
+             The original expression string provided at initialization.
+        """
         return self._expression_str
 
     @expression.setter
@@ -334,7 +357,14 @@ def __dir__(self) -> list[str]:
         return super().__dir__() + list(self._parameters.keys())
 
     def __repr__(self) -> str:
-        """Repr function."""
+        """
+        Return a string representation of the ExpressionComponent.
+
+        Returns
+        -------
+        str
+            String representation of the ExpressionComponent.
+        """
         param_str = ', '.join(f'{k}={v.value}' for k, v in self._parameters.items())
         return (
             f'{self.__class__.__name__}(\n'
diff --git a/src/easydynamics/settings/convolution_settings.py b/src/easydynamics/settings/convolution_settings.py
index a845fc91..3a4995c5 100644
--- a/src/easydynamics/settings/convolution_settings.py
+++ b/src/easydynamics/settings/convolution_settings.py
@@ -15,6 +15,7 @@ def __init__(
         self,
         upsample_factor: Numeric | None = 5,
         extension_factor: Numeric | None = 0.2,
+        suppress_warnings: bool = False,
         display_name: str | None = 'MyConvolutionSettings',
         unique_name: str | None = None,
     ) -> None:
@@ -27,6 +28,8 @@ def __init__(
             The factor by which to upsample the input data before convolution.
         extension_factor : Numeric | None, default=0.2
             The factor by which to extend the input data range before convolution.
+        suppress_warnings : bool, default=False
+            Whether to suppress warnings about wide or narrow peaks in the models.
         display_name : str | None, default='MyConvolutionSettings'
             Display name of the model.
         unique_name : str | None, default=None
@@ -36,7 +39,7 @@ def __init__(
         ------
         TypeError
             If upsample_factor is not a number or None. If extension_factor is not a number or
-            None.
+            None. If suppress_warnings is not a boolean.
         ValueError
             If upsample_factor is not greater than 1. If extension_factor is negative.
         """
@@ -61,6 +64,10 @@ def __init__(
                 raise ValueError('Upsample factor must be greater than 1.')
         self._upsample_factor = upsample_factor
 
+        if not isinstance(suppress_warnings, bool):
+            raise TypeError('suppress_warnings must be True or False.')
+        self._suppress_warnings = suppress_warnings
+
         self._convolution_plan_is_valid = False
 
     @property
@@ -184,6 +191,37 @@ def convolution_plan_is_valid(self, is_valid: bool) -> None:
             raise TypeError('convolution_plan_is_valid must be True or False.')
         self._convolution_plan_is_valid = is_valid
 
+    @property
+    def suppress_warnings(self) -> bool:
+        """
+        Get whether to suppress warnings.
+
+        Returns
+        -------
+        bool
+            Whether to suppress warnings.
+        """
+        return self._suppress_warnings
+
+    @suppress_warnings.setter
+    def suppress_warnings(self, suppress: bool) -> None:
+        """
+        Set whether to suppress warnings.
+
+        Parameters
+        ----------
+        suppress : bool
+            Whether to suppress warnings.
+
+        Raises
+        ------
+        TypeError
+            If suppress is not a bool.
+        """
+        if not isinstance(suppress, bool):
+            raise TypeError('suppress_warnings must be True or False.')
+        self._suppress_warnings = suppress
+
     def __repr__(self) -> str:
         """
         Return a string representation of the ConvolutionSettings.
@@ -196,5 +234,6 @@ def __repr__(self) -> str:
         return (
             f'{self.__class__.__name__}('
             f'upsample_factor={self.upsample_factor}, '
-            f'extension_factor={self.extension_factor})'
+            f'extension_factor={self.extension_factor}, '
+            f'suppress_warnings={self.suppress_warnings})'
         )
diff --git a/tests/integration/fitting/test_fitting_with_diffusion_model.py b/tests/integration/fitting/test_fitting_with_diffusion_model.py
index 22ca71f9..0b3abc82 100644
--- a/tests/integration/fitting/test_fitting_with_diffusion_model.py
+++ b/tests/integration/fitting/test_fitting_with_diffusion_model.py
@@ -61,7 +61,7 @@ def test_fitting_with_diffusion_model(self):
 
         assert fit_result_independent_single_Q.success
         assert fit_result_independent_single_Q.chi2 < 75.0
-        assert fit_result_independent_single_Q.reduced_chi < 0.4
+        assert fit_result_independent_single_Q.reduced_chi2 < 0.4
 
         diffusion_experiment = Experiment('Diffusion')
 
@@ -101,7 +101,7 @@ def test_fitting_with_diffusion_model(self):
 
         assert fit_result[0].success
         assert fit_result[0].chi2 < 43.0
-        assert fit_result[0].reduced_chi < 0.3
+        assert fit_result[0].reduced_chi2 < 0.3
 
         ###############
         # Diffusion model
@@ -142,7 +142,7 @@ def test_fitting_with_diffusion_model(self):
 
         assert fit_result[0].success
         assert fit_result[0].chi2 < 56.0
-        assert fit_result[0].reduced_chi < 0.4
+        assert fit_result[0].reduced_chi2 < 0.4
 
         pars = diffusion_model.get_all_parameters()
 
diff --git a/tests/performance_tests/convolution/convolution_width_thresholds.ipynb b/tests/performance_tests/convolution/convolution_width_thresholds.ipynb
index 8b72d888..fbb5df29 100644
--- a/tests/performance_tests/convolution/convolution_width_thresholds.ipynb
+++ b/tests/performance_tests/convolution/convolution_width_thresholds.ipynb
@@ -45,9 +45,8 @@
     "    sample_components=sample_components,\n",
     "    resolution_components=resolution_components,\n",
     "    energy=x,\n",
-    "    upsample_factor=None,\n",
     ")\n",
-    "\n",
+    "numerical_convolver.upsample_factor = None\n",
     "for gwidth in gaussian_widths:\n",
     "    sample_components.components[0].width = gwidth\n",
     "    y_analytical = analytical_convolver.convolution()\n",
@@ -74,14 +73,6 @@
     "plt.legend()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d6124d02",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -112,9 +103,8 @@
     "    sample_components=sample_components,\n",
     "    resolution_components=resolution_components,\n",
     "    energy=x,\n",
-    "    upsample_factor=None,\n",
     ")\n",
-    "\n",
+    "numerical_convolver.upsample_factor = None\n",
     "for gwidth, gcenter in zip(gaussian_widths, gaussian_centers, strict=True):\n",
     "    sample_components.components[0].width = gwidth\n",
     "    sample_components.components[0].center = gcenter\n",
@@ -145,7 +135,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "easydynamics_newbase",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -159,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.14.4"
   }
  },
  "nbformat": 4,
diff --git a/tests/unit/easydynamics/analysis/test_analysis.py b/tests/unit/easydynamics/analysis/test_analysis.py
index 91e8f795..64949fa6 100644
--- a/tests/unit/easydynamics/analysis/test_analysis.py
+++ b/tests/unit/easydynamics/analysis/test_analysis.py
@@ -2,7 +2,6 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from unittest.mock import MagicMock
-from unittest.mock import PropertyMock
 from unittest.mock import patch
 
 import numpy as np
@@ -273,111 +272,131 @@ def test_plot_data_and_model_invalid_add_background_raises(self, analysis):
         ):
             analysis.plot_data_and_model(add_background='not_a_boolean')
 
-    def test_plot_data_and_model_defaults(self, analysis):
-
+    @pytest.mark.parametrize(
+        'plot_components, expect_components',
+        [
+            (False, False),
+            (True, True),
+        ],
+        ids=[
+            'No components',
+            'With components',
+        ],
+    )
+    def test_plot_data_and_model(
+        self,
+        analysis,
+        plot_components,
+        expect_components,
+    ):
         # WHEN
-        # Create fake widget
         fake_widget = MagicMock()
         fake_widget.slider_toggler = MagicMock()
-        fake_widget.slider_toggler.value = None
 
-        # Create fake fig with required structure
         fake_fig = MagicMock()
         fake_fig.bottom_bar = [MagicMock()]
         fake_fig.bottom_bar[0].controls = {'test': fake_widget}
 
-        fake_data = MagicMock()
-        fake_data.coords = {'Q': 'Q_VALUES', 'energy': 'ENERGY_VALUES'}
-
-        analysis._create_model_array = MagicMock(return_value='MODEL')
         with (
             patch('plopp.slicer', return_value=fake_fig) as mock_slicer,
-            patch.object(
-                type(analysis.experiment),
-                'binned_data',
-                new_callable=PropertyMock,
-            ) as mock_binned,
             patch('easydynamics.analysis.analysis._in_notebook', return_value=True),
         ):
-            mock_binned.return_value = fake_data
             # THEN
-            fig = analysis.plot_data_and_model(plot_components=False)
+            fig = analysis.plot_data_and_model(plot_components=plot_components)
 
         # EXPECT
+        assert fig is fake_fig
+
         mock_slicer.assert_called_once()
-        assert fig == fake_fig
-        # Inspect arguments passed to slicer
+
         args, kwargs = mock_slicer.call_args
 
-        data_passed = args[0]
-        assert 'Data' in data_passed
-        assert 'Model' in data_passed
+        datagroup = args[0]
 
-        assert data_passed['Data'] == fake_data
-        assert data_passed['Model'] == 'MODEL'
+        assert isinstance(datagroup, sc.DataGroup)
 
-        # Check the default kwargs
-        assert kwargs['title'] == 'TestAnalysis'
-        assert kwargs['linestyle'] == {'Data': 'none', 'Model': '-'}
-        assert kwargs['marker'] == {'Data': 'o', 'Model': None}
-        assert kwargs['color'] == {'Data': 'black', 'Model': 'red'}
-        assert kwargs['markerfacecolor'] == {
-            'Data': 'none',
-            'Model': 'none',
-        }
+        assert 'Data' in datagroup
+        assert 'Model' in datagroup
+
+        component_keys = set(datagroup.keys()) - {'Data', 'Model'}
 
-    def test_plot_data_and_model_plot_components_true(self, analysis):
+        if expect_components:
+            assert len(component_keys) > 0
 
+            for key in component_keys:
+                assert kwargs['linestyle'][key] == '--'
+                assert kwargs['marker'][key] is None
+        else:
+            assert component_keys == set()
+
+        # Default plotting kwargs
+        assert kwargs['title'] == analysis.display_name
+
+        assert kwargs['linestyle']['Data'] == 'none'
+        assert kwargs['linestyle']['Model'] == '-'
+
+        assert kwargs['marker']['Data'] == 'o'
+        assert kwargs['marker']['Model'] is None
+
+        assert kwargs['color']['Data'] == 'black'
+        assert kwargs['color']['Model'] == 'red'
+
+        assert kwargs['markerfacecolor']['Data'] == 'none'
+        assert kwargs['markerfacecolor']['Model'] == 'none'
+
+        assert kwargs['keep'] == 'energy'
+
+        # Widget toggler updated
+        assert fake_widget.slider_toggler.value == '-o-'
+
+    def test_data_and_model_to_datagroup(self, analysis):
         # WHEN
-        # Create fake widget
-        fake_widget = MagicMock()
-        fake_widget.slider_toggler = MagicMock()
-        fake_widget.slider_toggler.value = None
+        energy = sc.array(dims=['energy'], values=[20.0, 30.0, 40.0], unit='meV')
+        datagroup = analysis.data_and_model_to_datagroup(energy=energy)
 
-        # Create fake fig with required structure
-        fake_fig = MagicMock()
-        fake_fig.bottom_bar = [MagicMock()]
-        fake_fig.bottom_bar[0].controls = {'test': fake_widget}
+        # EXPECT
+        assert isinstance(datagroup, sc.DataGroup)
+        assert 'Data' in datagroup
+        assert 'Model' in datagroup
+        assert sc.identical(datagroup['Data'], analysis.experiment.binned_data)
+        assert sc.identical(datagroup['Model'], analysis._create_model_array(energy=energy))
 
-        fake_data = MagicMock()
-        fake_data.coords = {'Q': 'Q_VALUES', 'energy': 'ENERGY_VALUES'}
+    def test_data_and_model_to_datagroup_no_data_raises(self, analysis):
+        # WHEN
+        analysis.experiment._binned_data = None
 
-        analysis._create_model_array = MagicMock(return_value='MODEL')
-        analysis._create_components_dataset = MagicMock(return_value={'Gaussian': 'GAUSS'})
+        # THEN EXPECT
+        with pytest.raises(
+            ValueError,
+            match='No data to include in DataGroup',
+        ):
+            analysis.data_and_model_to_datagroup()
+
+    def test_data_and_model_to_datagroup_no_Q_raises(self, analysis):
         with (
-            patch('plopp.slicer', return_value=fake_fig) as mock_slicer,
-            patch.object(
-                type(analysis.experiment),
-                'binned_data',
-                new_callable=PropertyMock,
-            ) as mock_binned,
-            patch('easydynamics.analysis.analysis._in_notebook', return_value=True),
+            patch.object(type(analysis), 'Q', new=None),
+            pytest.raises(
+                ValueError,
+                match='No Q values available for creating DataGroup',
+            ),
         ):
-            mock_binned.return_value = fake_data
-            # THEN
-            fig = analysis.plot_data_and_model(plot_components=True)
+            analysis.data_and_model_to_datagroup()
 
-        # EXPECT
-        mock_slicer.assert_called_once()
-        assert fig == fake_fig
-        # Inspect arguments passed to slicer
-        args, kwargs = mock_slicer.call_args
+    def test_data_and_model_to_datagroup_add_background_not_bool_raises(self, analysis):
+        # WHEN / THEN / EXPECT
+        with pytest.raises(
+            TypeError,
+            match=r'add_background must be True or False',
+        ):
+            analysis.data_and_model_to_datagroup(add_background='not_a_boolean')
 
-        data_passed = args[0]
-        assert 'Data' in data_passed
-        assert 'Model' in data_passed
-
-        assert data_passed['Data'] == fake_data
-        assert data_passed['Model'] == 'MODEL'
-        # Check the default kwargs
-        assert kwargs['title'] == 'TestAnalysis'
-        assert kwargs['linestyle'] == {'Data': 'none', 'Model': '-', 'Gaussian': '--'}
-        assert kwargs['marker'] == {'Data': 'o', 'Model': None, 'Gaussian': None}
-        assert kwargs['color'] == {'Data': 'black', 'Model': 'red'}
-        assert kwargs['markerfacecolor'] == {
-            'Data': 'none',
-            'Model': 'none',
-        }
+    def test_data_and_model_to_datagroup_include_components_not_bool_raises(self, analysis):
+        # WHEN / THEN / EXPECT
+        with pytest.raises(
+            TypeError,
+            match=r'include_components must be True or False',
+        ):
+            analysis.data_and_model_to_datagroup(include_components='not_a_boolean')
 
     def test_parameters_to_dataset(self, analysis):
         # WHEN
diff --git a/tests/unit/easydynamics/analysis/test_analysis1d.py b/tests/unit/easydynamics/analysis/test_analysis1d.py
index 234e166c..49f08465 100644
--- a/tests/unit/easydynamics/analysis/test_analysis1d.py
+++ b/tests/unit/easydynamics/analysis/test_analysis1d.py
@@ -240,16 +240,6 @@ def test_plot_raises_if_no_data(self, analysis1d):
 
     def test_plot_calls_plopp_with_correct_arguments(self, analysis1d):
         # WHEN
-
-        # Mock the data and model components to be plotted
-        fake_model = sc.DataArray(data=sc.array(dims=['energy'], values=[1, 2, 3]))
-        analysis1d._create_model_array = MagicMock(return_value=fake_model)
-
-        fake_components = sc.Dataset({
-            'Component1': sc.DataArray(data=sc.array(dims=['energy'], values=[0.1, 0.2, 0.3]))
-        })
-        analysis1d._create_components_dataset_single_Q = MagicMock(return_value=fake_components)
-
         fake_fig = object()
 
         with patch('plopp.plot', return_value=fake_fig) as mock_plot:
@@ -258,23 +248,100 @@ def test_plot_calls_plopp_with_correct_arguments(self, analysis1d):
 
         # EXPECT
 
-        # Ensure component dataset created
-        analysis1d._create_components_dataset_single_Q.assert_called_once()
-
-        # Ensure plot called
+        # plot called once
         mock_plot.assert_called_once()
 
-        # Inspect arguments
-        args, _ = mock_plot.call_args
+        # Extract arguments
+        args, kwargs = mock_plot.call_args
+
+        datagroup = args[0]
 
-        dataset_passed = args[0]
+        # Basic structure
+        assert isinstance(datagroup, sc.DataGroup)
 
-        assert 'Data' in dataset_passed
-        assert 'Model' in dataset_passed
-        assert 'Component1' in dataset_passed
+        assert 'Data' in datagroup
+        assert 'Model' in datagroup
 
+        # Gaussian sample component should also be included
+        # because plot_components=True by default
+        component_names = set(datagroup.keys()) - {'Data', 'Model'}
+        assert len(component_names) > 0
+
+        # Check plotting defaults
+        assert kwargs['title'] == analysis1d.display_name
+
+        assert kwargs['linestyle']['Data'] == 'none'
+        assert kwargs['marker']['Data'] == 'o'
+        assert kwargs['color']['Data'] == 'black'
+
+        assert kwargs['linestyle']['Model'] == '-'
+        assert kwargs['color']['Model'] == 'red'
+
+        # Return value propagated
         assert result is fake_fig
 
+    def test_data_and_model_to_datagroup(self, analysis1d):
+        # WHEN
+        energy = sc.array(dims=['energy'], values=[20.0, 30.0, 40.0], unit='meV')
+
+        # THEN
+        datagroup = analysis1d.data_and_model_to_datagroup(energy=energy)
+
+        # EXPECT
+        assert isinstance(datagroup, sc.DataGroup)
+        assert 'Data' in datagroup
+        assert 'Model' in datagroup
+        assert sc.identical(
+            datagroup['Data'],
+            analysis1d.experiment.binned_data['Q', analysis1d.Q_index],
+        )
+        assert sc.identical(datagroup['Model'], analysis1d._create_model_array(energy=energy))
+
+    def test_data_and_model_to_datagroup_no_data_raises(self, analysis1d):
+        # WHEN
+        analysis1d.experiment._binned_data = None
+
+        # THEN EXPECT
+        with pytest.raises(
+            ValueError,
+            match='No data to include in DataGroup',
+        ):
+            analysis1d.data_and_model_to_datagroup()
+
+    def test_data_and_model_to_datagroup_no_Q_raises(self, analysis1d):
+        with (
+            patch.object(type(analysis1d), 'Q', new=None),
+            pytest.raises(
+                ValueError,
+                match='No Q values available for creating DataGroup',
+            ),
+        ):
+            analysis1d.data_and_model_to_datagroup()
+
+    def test_data_and_model_to_datagroup_add_background_not_bool_raises(self, analysis1d):
+        # WHEN / THEN / EXPECT
+        with pytest.raises(
+            TypeError,
+            match=r'add_background must be True or False',
+        ):
+            analysis1d.data_and_model_to_datagroup(add_background='not_a_boolean')
+
+    def test_data_and_model_to_datagroup_include_components_not_bool_raises(self, analysis1d):
+        # WHEN / THEN / EXPECT
+        with pytest.raises(
+            TypeError,
+            match=r'include_components must be True or False',
+        ):
+            analysis1d.data_and_model_to_datagroup(include_components='not_a_boolean')
+
+    def test_plot_data_and_model_no_Q_index_raises(self, analysis1d):
+        # WHEN
+        analysis1d._Q_index = None
+
+        # THEN EXPECT
+        with pytest.raises(ValueError, match='Q_index must be set'):
+            analysis1d.plot_data_and_model()
+
     def test_fix_and_free_offset(self, analysis1d):
         # WHEN
 
diff --git a/tests/unit/easydynamics/analysis/test_analysis_base.py b/tests/unit/easydynamics/analysis/test_analysis_base.py
index a7241831..079404e4 100644
--- a/tests/unit/easydynamics/analysis/test_analysis_base.py
+++ b/tests/unit/easydynamics/analysis/test_analysis_base.py
@@ -6,12 +6,14 @@
 
 import numpy as np
 import pytest
+import scipp as sc
 from easyscience.variable import Parameter
 
 from easydynamics.analysis.analysis_base import AnalysisBase
 from easydynamics.experiment import Experiment
 from easydynamics.sample_model import InstrumentModel
 from easydynamics.sample_model import SampleModel
+from easydynamics.sample_model.components.gaussian import Gaussian
 from easydynamics.settings.convolution_settings import ConvolutionSettings
 from easydynamics.settings.detailed_balance_settings import DetailedBalanceSettings
 
@@ -29,6 +31,33 @@ def analysis_base(self):
             instrument_model=instrument_model,
         )
 
+    @pytest.fixture
+    def analysis_base_with_components(self):
+        Q = sc.array(dims=['Q'], values=[1, 2], unit='1/Angstrom')
+        energy = sc.array(dims=['energy'], values=[10.0, 20.0, 30.0], unit='meV')
+        data = sc.array(
+            dims=['Q', 'energy'],
+            values=[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+            variances=[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
+        )
+
+        data_array = sc.DataArray(data=data, coords={'Q': Q, 'energy': energy})
+
+        experiment = Experiment(data=data_array)
+
+        comp1 = Gaussian(area=1, width=2, center=3)
+        comp2 = Gaussian(area=4, width=5, center=6)
+        sample_model = SampleModel()
+        sample_model.append_component(comp1)
+        sample_model.append_component(comp2)
+        instrument_model = InstrumentModel()
+        return AnalysisBase(
+            display_name='TestAnalysis',
+            experiment=experiment,
+            sample_model=sample_model,
+            instrument_model=instrument_model,
+        )
+
     def test_init(self, analysis_base):
         # WHEN THEN
 
@@ -369,6 +398,95 @@ def test_normalize_resolution_calls_instrument_model(self, analysis_base):
             analysis_base.normalize_resolution()
             mock_normalize_resolution.assert_called_once()
 
+    def test_get_parameters_near_bounds_no_bounds(self, analysis_base_with_components):
+        # WHEN THEN
+        near_bounds = analysis_base_with_components.get_parameters_near_bounds()
+
+        # EXPECT
+        assert isinstance(near_bounds, list)
+        assert len(near_bounds) == 0
+
+    def test_get_parameters_near_bounds_at_bounds(self, analysis_base_with_components):
+        # WHEN
+        components = analysis_base_with_components.sample_model.get_component_collection(
+            Q_index=0
+        ).components
+        components[0].area.min = 1.0
+        components[1].center.max = 6.0
+
+        # THEN
+        near_bounds = analysis_base_with_components.get_parameters_near_bounds()
+
+        # EXPECT
+        assert isinstance(near_bounds, list)
+        assert len(near_bounds) == 2
+        assert components[0].area in near_bounds
+        assert components[1].center in near_bounds
+
+    def test_get_parameters_near_bounds_with_tolerances(self, analysis_base_with_components):
+        # WHEN
+        components = analysis_base_with_components.sample_model.get_component_collection(
+            Q_index=0
+        ).components
+        components[0].area.min = 0.99999
+        components[1].center.max = 6.00001
+
+        # THEN
+        near_bounds = analysis_base_with_components.get_parameters_near_bounds()
+
+        # EXPECT
+        assert isinstance(near_bounds, list)
+        assert len(near_bounds) == 2
+        assert components[0].area in near_bounds
+        assert components[1].center in near_bounds
+
+    @pytest.mark.parametrize(
+        'rtol, atol, expected_param, expected_error',
+        [
+            ('1e-5', 1e-8, 'rtol', TypeError),  # str
+            (None, 1e-8, 'rtol', TypeError),  # None
+            (1e-5, '1e-8', 'atol', TypeError),  # str
+            (1e-5, None, 'atol', TypeError),  # None
+            (-1e-5, 1e-8, 'rtol', ValueError),  # negative rtol
+            (1e-5, -1e-8, 'atol', ValueError),  # negative atol
+        ],
+        ids=[
+            'rtol as string',
+            'rtol as None',
+            'atol as string',
+            'atol as None',
+            'rtol negative',
+            'atol negative',
+        ],
+    )
+    def test_get_parameters_near_bounds_errors(
+        self, analysis_base_with_components, rtol, atol, expected_param, expected_error
+    ):
+        with pytest.raises(expected_error) as exc:
+            analysis_base_with_components.get_parameters_near_bounds(
+                rtol=rtol,
+                atol=atol,
+            )
+
+        assert expected_param in str(exc.value)
+
+    def test_not_finite_parameters(self, analysis_base_with_components):
+        # WHEN
+        components = analysis_base_with_components.sample_model.get_component_collection(
+            Q_index=0
+        ).components
+        components[0].area.value = np.inf
+        components[1].center.value = np.nan
+
+        # THEN
+        near_bounds = analysis_base_with_components.get_parameters_near_bounds()
+
+        # EXPECT
+        assert isinstance(near_bounds, list)
+        assert len(near_bounds) == 2
+        assert components[0].area in near_bounds
+        assert components[1].center in near_bounds
+
     #############
     # Private methods
     #############
diff --git a/tests/unit/easydynamics/analysis/test_fit_binding.py b/tests/unit/easydynamics/analysis/test_fit_binding.py
new file mode 100644
index 00000000..a59e0f70
--- /dev/null
+++ b/tests/unit/easydynamics/analysis/test_fit_binding.py
@@ -0,0 +1,289 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+from unittest.mock import Mock
+
+import pytest
+
+from easydynamics.analysis.fit_binding import FitBinding
+from easydynamics.sample_model.components.gaussian import Gaussian
+from easydynamics.sample_model.diffusion_model.brownian_translational_diffusion import (
+    BrownianTranslationalDiffusion,
+)
+
+
+class TestFitBinding:
+    @pytest.fixture
+    def fit_binding(self):
+        model = Gaussian()
+        return FitBinding(parameter_name='parameter1', model=model)
+
+    @pytest.fixture
+    def diffusion_fit_binding(self):
+        model = BrownianTranslationalDiffusion()
+        return FitBinding(parameter_name='parameter3', model=model)
+
+    def test_initialization(self, fit_binding):
+        # WHEN THEN EXPECT
+        assert isinstance(fit_binding, FitBinding)
+        assert fit_binding.parameter_name == 'parameter1'
+        assert isinstance(fit_binding.model, Gaussian)
+        assert fit_binding.modes is None
+
+    @pytest.mark.parametrize(
+        'parameter_name, model, modes, error_msg',
+        [
+            # parameter_name errors
+            (123, Gaussian(), None, 'parameter_name must be a string'),
+            (None, Gaussian(), None, 'parameter_name must be a string'),
+            # model errors
+            (
+                'param',
+                123,
+                None,
+                'model must be a ModelComponent, ComponentCollection, or DiffusionModelBase',
+            ),
+            (
+                'param',
+                'not_a_model',
+                None,
+                'model must be a ModelComponent, ComponentCollection, or DiffusionModelBase',
+            ),
+            # modes type errors
+            (
+                'param',
+                Gaussian(),
+                123,
+                'modes must be a string, list of strings, or None',
+            ),
+            (
+                'param',
+                Gaussian(),
+                {'mode': 'area'},
+                'modes must be a string, list of strings, or None',
+            ),
+            # modes list content errors
+            (
+                'param',
+                Gaussian(),
+                ['area', 123],
+                'All modes in the list must be strings',
+            ),
+            ('param', Gaussian(), [None], 'All modes in the list must be strings'),
+        ],
+    )
+    def test_fitbinding_init_errors(self, parameter_name, model, modes, error_msg):
+        with pytest.raises(TypeError, match=error_msg):
+            FitBinding(
+                parameter_name=parameter_name,
+                model=model,
+                modes=modes,
+            )
+
+    # ------------------------------------------------------------------
+    # Properties
+    # ------------------------------------------------------------------
+
+    def test_parameter_name_setter(self, fit_binding):
+        # WHEN
+        fit_binding.parameter_name = 'new_parameter'
+
+        # THEN EXPECT
+        assert fit_binding.parameter_name == 'new_parameter'
+
+    def test_parameter_name_setter_errors(self, fit_binding):
+        with pytest.raises(TypeError, match='parameter_name must be a string'):
+            fit_binding.parameter_name = 123
+
+    def test_model_setter(self, fit_binding):
+        # WHEN
+        model = BrownianTranslationalDiffusion()
+
+        # THEN
+        fit_binding.model = model
+
+        # EXPECT
+        assert fit_binding.model is model
+
+    def test_model_setter_errors(self, fit_binding):
+        with pytest.raises(
+            TypeError,
+            match='model must be a ModelComponent, ComponentCollection, or DiffusionModelBase',
+        ):
+            fit_binding.model = 'not_a_model'
+
+    def test_modes_setter(self, fit_binding):
+        # WHEN
+        fit_binding.modes = 'area'
+
+        # THEN EXPECT
+        assert fit_binding.modes == ['area']
+
+        # WHEN
+        fit_binding.modes = ['area', 'width']
+
+        # THEN EXPECT
+        assert fit_binding.modes == ['area', 'width']
+
+    def test_modes_setter_errors(self, fit_binding):
+        with pytest.raises(TypeError, match='modes must be a string, list of strings, or None'):
+            fit_binding.modes = 123
+
+        with pytest.raises(TypeError, match='modes must be a string, list of strings, or None'):
+            fit_binding.modes = {'mode': 'area'}
+
+        with pytest.raises(TypeError, match='All modes in the list must be strings'):
+            fit_binding.modes = ['area', 123]
+
+        with pytest.raises(TypeError, match='All modes in the list must be strings'):
+            fit_binding.modes = [None]
+
+    # ------------------------------------------------------------------
+    # Other methods
+    # ------------------------------------------------------------------
+
+    def test_build_callables_component(self, fit_binding):
+        # WHEN
+        mock_model = Mock()
+        mock_model.evaluate.return_value = 1.0
+        fit_binding._model = mock_model
+
+        # THEN
+        callables = fit_binding.build_callables()
+
+        # EXPECT
+        assert len(callables) == 1
+        assert callable(callables[0])
+        assert callables[0](0) == pytest.approx(1.0)
+        mock_model.evaluate.assert_called_once_with(0)
+
+    def test_build_callables_diffusion(self, diffusion_fit_binding):
+        # WHEN
+        mock_model = Mock(spec=BrownianTranslationalDiffusion)
+        mock_model.calculate_QISF.return_value = 2.0
+        mock_model.scale.value = 3.0
+        mock_model.calculate_width.return_value = 0.5
+        diffusion_fit_binding._model = mock_model
+
+        # THEN
+        callables = diffusion_fit_binding.build_callables()
+
+        # EXPECT
+        assert len(callables) == 2
+        assert callable(callables[0])
+        assert callable(callables[1])
+        assert callables[0](0) == pytest.approx(6.0)  # 2.0 * 3.0
+        assert callables[1](0) == pytest.approx(0.5)
+        mock_model.calculate_QISF.assert_called_once_with(0)
+        mock_model.calculate_width.assert_called_once_with(0)
+
+    def test_build_callables_diffusion_with_modes(self, diffusion_fit_binding):
+        # WHEN
+        diffusion_fit_binding.modes = 'area'
+        mock_model = Mock(spec=BrownianTranslationalDiffusion)
+        mock_model.calculate_QISF.return_value = 2.0
+        mock_model.scale.value = 3.0
+        diffusion_fit_binding._model = mock_model
+
+        # THEN
+        callables = diffusion_fit_binding.build_callables()
+
+        # EXPECT
+        assert len(callables) == 1
+        assert callable(callables[0])
+        assert callables[0](0) == pytest.approx(6.0)  # 2.0 * 3.0
+        mock_model.calculate_QISF.assert_called_once_with(0)
+
+    def test_get_model_names(self, fit_binding):
+        # WHEN THEN
+        model_names = fit_binding.get_model_names()
+
+        # EXPECT
+        assert model_names == ['Gaussian']
+
+    def test_get_model_names_diffusion(self, diffusion_fit_binding):
+        # WHEN THEN
+        model_names = diffusion_fit_binding.get_model_names()
+
+        # EXPECT
+        assert model_names == [
+            'BrownianTranslationalDiffusion area',
+            'BrownianTranslationalDiffusion width',
+        ]
+
+    def test_get_parameter_names(self, fit_binding):
+        # WHEN THEN
+        parameter_names = fit_binding.get_parameter_names()
+
+        # EXPECT
+        assert parameter_names == ['parameter1']
+
+    def test_get_parameter_names_diffusion(self, diffusion_fit_binding):
+        # WHEN THEN
+        parameter_names = diffusion_fit_binding.get_parameter_names()
+
+        # EXPECT
+        assert parameter_names == ['parameter3 area', 'parameter3 width']
+
+    # ------------------------------------------------------------------
+    # Private methods
+    # ------------------------------------------------------------------
+
+    def test_build_diffusion_callable(self, diffusion_fit_binding):
+
+        # WHEN
+        mock_model = Mock()
+        mock_model.calculate_QISF.return_value = 2.0
+        mock_model.scale.value = 3.0
+        mock_model.calculate_width.return_value = 0.5
+        diffusion_fit_binding._model = mock_model
+
+        # THEN
+        area_callable = diffusion_fit_binding._build_diffusion_callable(mode='area')
+        width_callable = diffusion_fit_binding._build_diffusion_callable(mode='width')
+
+        # EXPECT
+        assert area_callable(0) == pytest.approx(6.0)  # 2.0 * 3.0
+        mock_model.calculate_QISF.assert_called_once_with(0)
+
+        assert width_callable(0) == pytest.approx(0.5)
+        mock_model.calculate_width.assert_called_once_with(0)
+
+        # THEN
+        result_area = area_callable(0, unused_arg=123)
+        result_width = width_callable(0, unused_arg=123)
+
+        # EXPECT
+        assert result_area == pytest.approx(6.0)  # Should ignore unused_arg
+        assert result_width == pytest.approx(0.5)  # Should ignore unused_arg
+
+    def test_build_diffusion_callable_errors(self, diffusion_fit_binding):
+        with pytest.raises(ValueError, match='Unknown diffusion mode: invalid_mode'):
+            diffusion_fit_binding._build_diffusion_callable(mode='invalid_mode')
+
+    def test_get_modes(self, diffusion_fit_binding):
+        # WHEN
+        modes = diffusion_fit_binding._get_modes()
+
+        # EXPECT
+        assert modes == ['area', 'width']
+
+        # THEN
+        diffusion_fit_binding.modes = 'area'
+        modes = diffusion_fit_binding._get_modes()
+        # EXPECT
+        assert modes == ['area']
+
+    # ------------------------------------------------------------------
+    # dunder methods
+    # ------------------------------------------------------------------
+    def test_repr(self, fit_binding):
+        # WHEN
+        repr_str = repr(fit_binding)
+
+        # THEN EXPECT
+        assert 'FitBinding' in repr_str
+        assert 'parameter_name=parameter1' in repr_str
+        assert 'model=Gaussian' in repr_str
+        assert 'modes=None' in repr_str
diff --git a/tests/unit/easydynamics/analysis/test_parameter_analysis.py b/tests/unit/easydynamics/analysis/test_parameter_analysis.py
new file mode 100644
index 00000000..94fe9ddb
--- /dev/null
+++ b/tests/unit/easydynamics/analysis/test_parameter_analysis.py
@@ -0,0 +1,885 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+from unittest.mock import MagicMock
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+import scipp as sc
+
+from easydynamics.analysis.analysis import Analysis
+from easydynamics.analysis.fit_binding import FitBinding
+from easydynamics.analysis.parameter_analysis import ParameterAnalysis
+from easydynamics.sample_model.components.gaussian import Gaussian
+from easydynamics.sample_model.components.polynomial import Polynomial
+from easydynamics.sample_model.diffusion_model.brownian_translational_diffusion import (
+    BrownianTranslationalDiffusion,
+)
+
+
+class TestParameterAnalysis:
+    @pytest.fixture
+    def dataset(self):
+        Q = sc.array(dims=['Q'], values=[0.1, 0.2])
+        return sc.Dataset(
+            data={
+                'parameter1': sc.DataArray(
+                    data=sc.array(dims=['Q'], values=[1.0, 2.0], variances=[0.1, 0.2], unit='meV'),
+                    coords={'Q': Q},
+                ),
+                'parameter2': sc.DataArray(
+                    data=sc.array(
+                        dims=['Q'],
+                        values=[1.5, 2.5],
+                        variances=[0.15, 0.25],
+                        unit='1/meV',
+                    ),
+                    coords={'Q': Q},
+                ),
+                'parameter3 area': sc.DataArray(
+                    data=sc.array(dims=['Q'], values=[4.0, 5.0], variances=[0.3, 0.5], unit='meV'),
+                    coords={'Q': Q},
+                ),
+                'parameter3 width': sc.DataArray(
+                    data=sc.array(dims=['Q'], values=[6.0, 7.0], variances=[0.6, 0.7], unit='meV'),
+                    coords={'Q': Q},
+                ),
+            }
+        )
+
+    @pytest.fixture
+    def mock_model_dataset(self):
+        return sc.Dataset({
+            'Polynomial': sc.DataArray(data=sc.array(dims=['Q'], values=[1.1, 2.1], unit='meV')),
+            'BrownianTranslationalDiffusion area': sc.DataArray(
+                data=sc.array(dims=['Q'], values=[6.1, 7.1], unit='meV')
+            ),
+            'BrownianTranslationalDiffusion width': sc.DataArray(
+                data=sc.array(dims=['Q'], values=[8.1, 9.1], unit='meV')
+            ),
+        })
+
+    @pytest.fixture
+    def parameter_analysis(self, dataset):
+        model = Polynomial(coefficients=[1.0, 0.5])
+        diffusion_model = BrownianTranslationalDiffusion()
+
+        fit_binding1 = FitBinding(parameter_name='parameter1', model=model)
+        fit_binding2 = FitBinding(parameter_name='parameter3', model=diffusion_model)
+
+        return ParameterAnalysis(parameters=dataset, bindings=[fit_binding1, fit_binding2])
+
+    def test_initialization(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        assert isinstance(parameter_analysis, ParameterAnalysis)
+        assert len(parameter_analysis.bindings) == 2
+        assert parameter_analysis.bindings[0].parameter_name == 'parameter1'
+        assert parameter_analysis.bindings[1].parameter_name == 'parameter3'
+
+    def test_parameter_property(self, parameter_analysis):
+        # WHEN
+        parameters = parameter_analysis.parameters
+
+        # THEN EXPECT
+        assert isinstance(parameters, sc.Dataset)
+        assert set(parameters.keys()) == {
+            'parameter1',
+            'parameter2',
+            'parameter3 area',
+            'parameter3 width',
+        }
+
+        # WHEN
+        Q = sc.array(dims=['Q'], values=[0.1, 0.2])
+        new_data = sc.Dataset(
+            data={
+                'parameter4': sc.DataArray(
+                    data=sc.array(
+                        dims=['Q'],
+                        values=[71.0, 12.0],
+                        variances=[1.1, 2.2],
+                        unit='meV',
+                    ),
+                    coords={'Q': Q},
+                ),
+                'parameter5': sc.DataArray(
+                    data=sc.array(
+                        dims=['Q'],
+                        values=[8.5, 0.5],
+                        variances=[2.15, 1.25],
+                        unit='1/meV',
+                    ),
+                    coords={'Q': Q},
+                ),
+            }
+        )
+
+        # THEN
+        parameter_analysis.parameters = new_data
+
+        # EXPECT
+        assert parameter_analysis.parameters is new_data
+
+    def test_bindings_property(self, parameter_analysis):
+        # WHEN
+        bindings = parameter_analysis.bindings
+
+        # THEN EXPECT
+        assert isinstance(bindings, list)
+        assert len(bindings) == 2
+        assert all(isinstance(b, FitBinding) for b in bindings)
+
+        # WHEN
+        model = Polynomial(coefficients=[2.0, 1.0])
+        new_binding = FitBinding(parameter_name='parameter2', model=model)
+        parameter_analysis.bindings = new_binding
+
+        # THEN EXPECT
+        assert parameter_analysis.bindings == [new_binding]
+
+    def test_fit_no_bindings_raises(self, parameter_analysis):
+        # WHEN
+
+        # THEN
+        parameter_analysis.bindings = None
+
+        # EXPECT
+        with pytest.raises(ValueError, match='No fit bindings provided'):
+            parameter_analysis.fit()
+
+    def test_fit_no_parameters_raises(self, parameter_analysis):
+        # WHEN
+
+        # THEN
+        parameter_analysis.parameters = None
+
+        # EXPECT
+        with pytest.raises(ValueError, match='No parameters Dataset provided'):
+            parameter_analysis.fit()
+
+    def test_fit_wrong_parameter_name_raises(self, parameter_analysis):
+        # WHEN
+        model = Polynomial(coefficients=[2.0, 1.0])
+        incorrect_binding = FitBinding(parameter_name='nonexistent_parameter', model=model)
+        parameter_analysis.bindings = incorrect_binding
+
+        # THEN EXPECT
+        with pytest.raises(
+            ValueError,
+            match="Parameter 'nonexistent_parameter' from binding",
+        ):
+            parameter_analysis.fit()
+
+    def test_fit_success(self, parameter_analysis):
+        # WHEN
+        mock_result = MagicMock()
+
+        with patch('easydynamics.analysis.parameter_analysis.MultiFitter') as MockMultiFitter:
+            instance = MockMultiFitter.return_value
+            instance.fit.return_value = mock_result
+
+            # THEN
+            result = parameter_analysis.fit()
+
+            # EXPECT
+            assert MockMultiFitter.called
+
+            kwargs = MockMultiFitter.call_args.kwargs
+            assert 'fit_objects' in kwargs
+            assert 'fit_functions' in kwargs
+
+            # Expect 3 fits:
+            # - parameter1 → 1 callable
+            # - parameter3 → 2 callables (area + width)
+            assert len(kwargs['fit_objects']) == 3
+            assert len(kwargs['fit_functions']) == 3
+
+            # --- Fit called correctly ---
+            instance.fit.assert_called_once()
+
+            call_kwargs = instance.fit.call_args.kwargs
+
+            x = call_kwargs['x']
+            y = call_kwargs['y']
+            w = call_kwargs['weights']
+
+            assert len(x) == 3
+            assert len(y) == 3
+            assert len(w) == 3
+
+            # Check one concrete value
+            np.testing.assert_allclose(x[0], [0.1, 0.2])
+            np.testing.assert_allclose(y[0], [1.0, 2.0])
+
+            expected_w = 1 / np.sqrt([0.1, 0.2])
+            np.testing.assert_allclose(w[0], expected_w)
+
+            assert result is mock_result
+
+    def test_plot_not_in_notebook_raises(self, parameter_analysis):
+        # WHEN / THEN / EXPECT
+        with (
+            patch(
+                'easydynamics.analysis.parameter_analysis._in_notebook',
+                return_value=False,
+            ),
+            pytest.raises(
+                RuntimeError,
+                match=r'can only be used in a Jupyter notebook environment',
+            ),
+        ):
+            parameter_analysis.plot()
+
+    def test_plot_no_parameters_raises(self, parameter_analysis):
+        # WHEN
+        parameter_analysis.parameters = None
+
+        # THEN EXPECT
+        with (
+            patch(
+                'easydynamics.analysis.parameter_analysis._in_notebook',
+                return_value=True,
+            ),
+            pytest.raises(ValueError, match=r'No parameters available to plot.'),
+        ):
+            parameter_analysis.plot()
+
+    def test_plot_incompatible_units_raises(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        with (
+            patch(
+                'easydynamics.analysis.parameter_analysis._in_notebook',
+                return_value=True,
+            ),
+            pytest.raises(ValueError, match=r'Units are not compatible'),
+        ):
+            # parameter1 in meV, parameter2 in 1/meV
+            parameter_analysis.plot(names=['parameter1', 'parameter2'])
+
+    @patch('easydynamics.analysis.parameter_analysis._in_notebook', return_value=True)
+    @patch('easydynamics.analysis.parameter_analysis.pp.plot')
+    def test_plot_calls_dependencies_correctly_names_none(
+        self, mock_plot, mock_notebook, parameter_analysis, mock_model_dataset, dataset
+    ):
+        # WHEN
+        # ensure compatible units for all parameters
+        dataset.pop('parameter2')
+        parameter_analysis.parameters = dataset
+
+        # Mock calculate_model_dataset
+        parameter_analysis.calculate_model_dataset = MagicMock(return_value=mock_model_dataset)
+
+        # THEN
+        result = parameter_analysis.plot()
+
+        # EXPECT
+
+        # 1. Notebook check
+        mock_notebook.assert_called_once()
+
+        # 2. Model dataset calculation
+        parameter_analysis.calculate_model_dataset.assert_called_once_with(
+            parameter_analysis.bindings
+        )
+
+        # 3. Plot called
+        mock_plot.assert_called_once()
+
+        # Extract call args
+        args, kwargs = mock_plot.call_args
+
+        # 4. Dataset passed correctly
+        dataset = args[0]
+        assert isinstance(dataset, sc.Dataset)
+
+        # Data keys
+        assert 'parameter1' in dataset
+        assert 'parameter3 area' in dataset
+        assert 'parameter3 width' in dataset
+
+        # Model keys (from bindings)
+        assert 'Polynomial' in dataset
+        assert 'BrownianTranslationalDiffusion area' in dataset
+        assert 'BrownianTranslationalDiffusion width' in dataset
+
+        # 5. Check some kwargs
+        assert kwargs['title'] == parameter_analysis.display_name
+
+        # Ensure styling dictionaries exist
+        for key in ['linestyle', 'marker', 'color', 'markerfacecolor']:
+            assert key in kwargs
+
+        # 6. Return value propagated
+        assert result == mock_plot.return_value
+
+    @patch('easydynamics.analysis.parameter_analysis._in_notebook', return_value=True)
+    @patch('easydynamics.analysis.parameter_analysis.pp.plot')
+    def test_plot_calls_dependencies_correctly(
+        self,
+        mock_plot,
+        mock_notebook,
+        parameter_analysis,
+        mock_model_dataset,
+    ):
+        # WHEN
+        # Mock calculate_model_dataset
+        parameter_analysis.calculate_model_dataset = MagicMock(return_value=mock_model_dataset)
+
+        # THEN
+        result = parameter_analysis.plot(names=['parameter1', 'parameter3 area'])
+
+        # EXPECT
+
+        # 1. Notebook check
+        mock_notebook.assert_called_once()
+
+        # 2. Model dataset calculation
+        parameter_analysis.calculate_model_dataset.assert_called_once_with(
+            parameter_analysis.bindings
+        )
+
+        # 3. Plot called
+        mock_plot.assert_called_once()
+
+        # Extract call args
+        args, kwargs = mock_plot.call_args
+
+        # 4. Dataset passed correctly
+        dataset = args[0]
+        assert isinstance(dataset, sc.Dataset)
+
+        # Data keys
+        assert 'parameter1' in dataset
+        assert 'parameter3 area' in dataset
+
+        # Model keys (from bindings)
+        assert 'Polynomial' in dataset
+        assert 'BrownianTranslationalDiffusion area' in dataset
+        assert 'BrownianTranslationalDiffusion width' not in dataset  # not requested
+
+        # 5. Check some kwargs
+        assert kwargs['title'] == parameter_analysis.display_name
+
+        # Ensure styling dictionaries exist
+        for key in ['linestyle', 'marker', 'color', 'markerfacecolor']:
+            assert key in kwargs
+
+        # 6. Return value propagated
+        assert result == mock_plot.return_value
+
+    @patch('easydynamics.analysis.parameter_analysis._in_notebook', return_value=True)
+    @patch('easydynamics.analysis.parameter_analysis.pp.plot')
+    def test_plot_no_bindings(
+        self, mock_plot, mock_notebook, parameter_analysis, mock_model_dataset, dataset
+    ):
+        # WHEN
+        # ensure compatible units for all parameters
+        dataset.pop('parameter2')
+        parameter_analysis.parameters = dataset
+        parameter_analysis.bindings = None
+
+        # Mock calculate_model_dataset
+        parameter_analysis.calculate_model_dataset = MagicMock(return_value=mock_model_dataset)
+
+        # THEN
+        result = parameter_analysis.plot()
+
+        # EXPECT
+
+        # 1. Notebook check
+        mock_notebook.assert_called_once()
+
+        # 2. Model dataset calculation
+        parameter_analysis.calculate_model_dataset.assert_not_called()
+
+        # 3. Plot called
+        mock_plot.assert_called_once()
+
+        # Extract call args
+        args, kwargs = mock_plot.call_args
+
+        # 4. Dataset passed correctly
+        dataset = args[0]
+        assert isinstance(dataset, sc.Dataset)
+
+        # Data keys
+        assert 'parameter1' in dataset
+        assert 'parameter3 area' in dataset
+        assert 'parameter3 width' in dataset
+
+        # Model keys should be absent
+        assert 'Polynomial' not in dataset
+        assert 'BrownianTranslationalDiffusion area' not in dataset
+        assert 'BrownianTranslationalDiffusion width' not in dataset
+
+        # 5. Check some kwargs
+        assert kwargs['title'] == parameter_analysis.display_name
+
+        # Ensure styling dictionaries exist
+        for key in ['linestyle', 'marker', 'color', 'markerfacecolor']:
+            assert key in kwargs
+
+        # 6. Return value propagated
+        assert result == mock_plot.return_value
+
+    @pytest.mark.parametrize(
+        'set_pars_none, bindings, expected_exception, match',
+        [
+            # No parameters
+            (
+                True,
+                [MagicMock(spec=FitBinding)],
+                ValueError,
+                'No parameters Dataset provided',
+            ),
+            # No bindings
+            (
+                False,
+                [],
+                ValueError,
+                'No fit bindings provided',
+            ),
+            # Wrong bindings type
+            (
+                False,
+                'not_a_list',
+                TypeError,
+                'bindings must be a list of FitBinding objects',
+            ),
+        ],
+        ids=['no_parameters', 'no_bindings', 'wrong_bindings_type'],
+    )
+    def test_calculate_model_dataset_invalid_inputs(
+        self, parameter_analysis, set_pars_none, bindings, expected_exception, match
+    ):
+        # WHEN
+        if set_pars_none:
+            parameter_analysis.parameters = None
+        else:
+            # keep existing valid dataset
+            pass
+
+        # THEN EXPECT
+        with pytest.raises(expected_exception, match=match):
+            parameter_analysis.calculate_model_dataset(bindings)
+
+    def test_calculate_model_dataset_missing_parameter_raises(self, parameter_analysis):
+        binding = parameter_analysis.bindings[0]
+
+        with (
+            patch.object(binding, 'get_parameter_names', return_value=['missing_name']),
+            patch.object(binding, 'get_model_names', return_value=['model']),
+            patch.object(binding, 'build_callables', return_value=[lambda x: x]),
+            pytest.raises(ValueError, match='not found in parameters Dataset'),
+        ):
+            parameter_analysis.calculate_model_dataset([binding])
+
+    def test_calculate_model_dataset_single_binding(self, parameter_analysis):
+        # WHEN
+        binding = parameter_analysis.bindings[0]
+
+        # Mock callable to return predictable output
+        mock_callable = MagicMock(return_value=np.array([10.0, 20.0]))
+
+        with (
+            patch.object(binding, 'build_callables', return_value=[mock_callable]),
+            patch.object(binding, 'get_model_names', return_value=['model1']),
+            patch.object(binding, 'get_parameter_names', return_value=['parameter1']),
+        ):
+            # THEN
+            result = parameter_analysis.calculate_model_dataset([binding])
+
+        # EXPECT
+        assert isinstance(result, sc.Dataset)
+        assert 'model1' in result
+
+        da = result['model1']
+
+        np.testing.assert_allclose(da.values, [10.0, 20.0])
+        np.testing.assert_allclose(da.coords['Q'].values, [0.1, 0.2])
+        assert da.unit == parameter_analysis.parameters['parameter1'].unit
+        mock_callable.assert_called_once()
+
+        args, kwargs = mock_callable.call_args
+        np.testing.assert_allclose(args[0], [0.1, 0.2])
+        assert kwargs == {}
+
+    def test_calculate_model_dataset_multiple_bindings(self, parameter_analysis):
+        # WHEN
+        binding1 = parameter_analysis.bindings[0]
+        binding2 = parameter_analysis.bindings[1]
+
+        # Mock callable to return predictable output
+        mock_callable1 = MagicMock(return_value=np.array([10.0, 20.0]))
+        mock_callable2 = MagicMock(return_value=np.array([30.0, 40.0]))
+
+        with (
+            patch.object(binding1, 'build_callables', return_value=[mock_callable1]),
+            patch.object(binding1, 'get_model_names', return_value=['model1']),
+            patch.object(binding1, 'get_parameter_names', return_value=['parameter1']),
+            patch.object(binding2, 'build_callables', return_value=[mock_callable2]),
+            patch.object(binding2, 'get_model_names', return_value=['model2']),
+            patch.object(binding2, 'get_parameter_names', return_value=['parameter2']),
+        ):
+            # THEN
+            result = parameter_analysis.calculate_model_dataset([binding1, binding2])
+
+        # EXPECT
+        assert isinstance(result, sc.Dataset)
+        assert 'model1' in result
+        assert 'model2' in result
+
+        da1 = result['model1']
+        da2 = result['model2']
+
+        np.testing.assert_allclose(da1.values, [10.0, 20.0])
+        np.testing.assert_allclose(da1.coords['Q'].values, [0.1, 0.2])
+        assert da1.unit == parameter_analysis.parameters['parameter1'].unit
+
+        np.testing.assert_allclose(da2.values, [30.0, 40.0])
+        np.testing.assert_allclose(da2.coords['Q'].values, [0.1, 0.2])
+        assert da2.unit == parameter_analysis.parameters['parameter2'].unit
+        mock_callable1.assert_called_once()
+        args, kwargs = mock_callable1.call_args
+        np.testing.assert_allclose(args[0], [0.1, 0.2])
+        assert kwargs == {}
+
+        mock_callable2.assert_called_once()
+        args, kwargs = mock_callable2.call_args
+        np.testing.assert_allclose(args[0], [0.1, 0.2])
+        assert kwargs == {}
+
+    def test_calculate_model_dataset_multiple_bindings_diffusion(self, parameter_analysis):
+        # WHEN
+        binding1 = parameter_analysis.bindings[0]
+        binding2 = parameter_analysis.bindings[1]
+
+        # Mock callable to return predictable output
+        mock_callable1 = MagicMock(return_value=np.array([10.0, 20.0]))
+        mock_callable2a = MagicMock(return_value=np.array([30.0, 40.0]))
+        mock_callable2w = MagicMock(return_value=np.array([50.0, 60.0]))
+
+        with (
+            patch.object(binding1, 'build_callables', return_value=[mock_callable1]),
+            patch.object(binding1, 'get_model_names', return_value=['model1']),
+            patch.object(binding1, 'get_parameter_names', return_value=['parameter1']),
+            patch.object(
+                binding2,
+                'build_callables',
+                return_value=[mock_callable2a, mock_callable2w],
+            ),
+            patch.object(binding2, 'get_model_names', return_value=['model2a', 'model2w']),
+            patch.object(
+                binding2,
+                'get_parameter_names',
+                return_value=['parameter3 area', 'parameter3 width'],
+            ),
+        ):
+            # THEN
+            result = parameter_analysis.calculate_model_dataset([binding1, binding2])
+
+        # EXPECT
+        assert isinstance(result, sc.Dataset)
+        assert 'model1' in result
+        assert 'model2a' in result
+        assert 'model2w' in result
+
+        da1 = result['model1']
+        da2a = result['model2a']
+        da2w = result['model2w']
+
+        np.testing.assert_allclose(da1.values, [10.0, 20.0])
+        np.testing.assert_allclose(da1.coords['Q'].values, [0.1, 0.2])
+        assert da1.unit == parameter_analysis.parameters['parameter1'].unit
+
+        np.testing.assert_allclose(da2a.values, [30.0, 40.0])
+        np.testing.assert_allclose(da2a.coords['Q'].values, [0.1, 0.2])
+        assert da2a.unit == parameter_analysis.parameters['parameter3 area'].unit
+
+        np.testing.assert_allclose(da2w.values, [50.0, 60.0])
+        np.testing.assert_allclose(da2w.coords['Q'].values, [0.1, 0.2])
+        assert da2w.unit == parameter_analysis.parameters['parameter3 width'].unit
+
+        args, kwargs = mock_callable1.call_args
+        np.testing.assert_allclose(args[0], [0.1, 0.2])
+        assert kwargs == {}
+
+        args, kwargs = mock_callable2a.call_args
+        np.testing.assert_allclose(args[0], [0.1, 0.2])
+        assert kwargs == {}
+
+        args, kwargs = mock_callable2w.call_args
+        np.testing.assert_allclose(args[0], [0.1, 0.2])
+        assert kwargs == {}
+
+    def test_append_binding(self, parameter_analysis):
+        # WHEN
+        model = Polynomial(coefficients=[2.0, 1.0])
+        new_binding = FitBinding(parameter_name='parameter2', model=model)
+
+        # THEN
+        parameter_analysis.append_binding(new_binding)
+
+        # EXPECT
+        assert len(parameter_analysis.bindings) == 3
+        assert parameter_analysis.bindings[-1] == new_binding
+
+    def test_append_binding_wrong_type_raises(self, parameter_analysis):
+        # WHEN
+        new_binding = 'not_a_fit_binding'
+
+        # THEN EXPECT
+        with pytest.raises(TypeError, match='binding must be a FitBinding object'):
+            parameter_analysis.append_binding(new_binding)
+
+    def test_clear_bindings(self, parameter_analysis):
+        # WHEN
+        parameter_analysis.clear_bindings()
+
+        # THEN EXPECT
+        assert len(parameter_analysis.bindings) == 0
+
+    def test_get_all_variables(self, parameter_analysis):
+        # WHEN
+
+        # THEN
+        variables = parameter_analysis.get_all_variables()
+
+        # EXPECT
+        expected_variables = []
+        expected_variables.extend(parameter_analysis.bindings[0].model.get_all_variables())
+        expected_variables.extend(parameter_analysis.bindings[1].model.get_all_variables())
+        assert set(variables) == set(expected_variables)
+
+    def test_get_all_variables_no_bindings(self, parameter_analysis):
+        # WHEN
+        parameter_analysis.clear_bindings()
+
+        # THEN EXPECT
+        variables = parameter_analysis.get_all_variables()
+        assert variables == []
+
+    def test_get_all_variables_overlapping_variables(self, parameter_analysis):
+        # WHEN
+        # Create two bindings with overlapping variables
+        model1 = Gaussian(display_name='model1')
+
+        binding1 = FitBinding(parameter_name='parameter1', model=model1)
+        binding2 = FitBinding(parameter_name='parameter2', model=model1)
+
+        parameter_analysis.bindings = [binding1, binding2]
+
+        # THEN
+        variables = parameter_analysis.get_all_variables()
+
+        # EXPECT
+        assert isinstance(variables, list)
+        assert set(variables) == set(model1.get_all_variables())
+        assert len(variables) == len(model1.get_all_variables())
+
+    @pytest.mark.parametrize(
+        'input_bindings, expected_length',
+        [
+            (None, 0),
+            ('single', 1),
+            ('list', 2),
+        ],
+    )
+    def test_verify_bindings_valid(self, parameter_analysis, input_bindings, expected_length):
+        # WHEN
+        b1 = parameter_analysis.bindings[0]
+        b2 = parameter_analysis.bindings[1]
+
+        if input_bindings == 'single':
+            input_bindings = b1
+        elif input_bindings == 'list':
+            input_bindings = [b1, b2]
+
+        # THEN
+        result = parameter_analysis._verify_bindings(input_bindings)
+
+        # EXPECT
+        assert isinstance(result, list)
+        assert len(result) == expected_length
+
+        if expected_length == 1:
+            assert result[0] is b1
+        if expected_length == 2:
+            assert result == [b1, b2]
+
+    @pytest.mark.parametrize(
+        'invalid_bindings',
+        [
+            'not_a_list_or_fit_binding',
+            [MagicMock(spec=FitBinding), 'not_a_fit_binding'],
+        ],
+        ids=['wrong_type', 'list_with_wrong_type'],
+    )
+    def test_verify_bindings_invalid(self, parameter_analysis, invalid_bindings):
+        # WHEN THEN EXPECT
+        with pytest.raises((ValueError, TypeError)):
+            parameter_analysis._verify_bindings(invalid_bindings)
+
+    def test_verify_parameters_none(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        assert parameter_analysis._verify_parameters(None) is None
+
+    def test_verify_parameters_wrong_type(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        with pytest.raises(
+            TypeError, match=r'parameters must be a sc.Dataset, an Analysis, or None'
+        ):
+            parameter_analysis._verify_parameters('not_a_dataset_or_analysis')
+
+    def test_verify_parameters_wrong_coordinate(self, parameter_analysis):
+        # WHEN
+        T = sc.array(dims=['T'], values=[0.1, 0.2])
+        wrong_dataset = sc.Dataset(
+            data={
+                'parameter1': sc.DataArray(
+                    data=sc.array(dims=['T'], values=[1.0, 2.0], variances=[0.1, 0.2], unit='meV'),
+                    coords={'T': T},
+                ),
+            }
+        )
+
+        # THEN EXPECT
+        with pytest.raises(ValueError, match="parameters must have a 'Q' coordinate"):
+            parameter_analysis._verify_parameters(wrong_dataset)
+
+    @pytest.mark.parametrize(
+        'input_type',
+        ['dataset', 'analysis'],
+    )
+    def test_verify_parameters_valid_inputs(self, parameter_analysis, dataset, input_type):
+        # WHEN
+        if input_type == 'dataset':
+            input_value = dataset
+        else:
+            mock_analysis = MagicMock(spec=Analysis)
+            mock_analysis.parameters_to_dataset.return_value = dataset
+            input_value = mock_analysis
+
+        # THEN
+        result = parameter_analysis._verify_parameters(input_value)
+
+        # EXPECT
+        assert result is dataset
+
+        if input_type == 'analysis':
+            input_value.parameters_to_dataset.assert_called_once()
+
+    def test_normalize_names_none(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        assert parameter_analysis._normalize_names(None) is None
+
+    def test_normalize_names_wrong_type(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        with pytest.raises(
+            ValueError, match=r'names must be a string, a list of strings, or None.'
+        ):
+            parameter_analysis._normalize_names(123)
+
+    def test_normalize_names_list_with_non_string(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        with pytest.raises(ValueError, match=r'All names in the list must be strings.'):
+            parameter_analysis._normalize_names(['parameter1', 123])
+
+    def test_normalize_names_nonexistent_parameter(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        with pytest.raises(
+            ValueError,
+            match=r"Parameter name 'nonexistent_parameter' not found in parameters Dataset.",
+        ):
+            parameter_analysis._normalize_names('nonexistent_parameter')
+
+        with pytest.raises(
+            ValueError,
+            match=r"Parameter name 'nonexistent_parameter' not found in parameters Dataset.",
+        ):
+            parameter_analysis._normalize_names(['parameter1', 'nonexistent_parameter'])
+
+    def test_normalize_names_valid_string(self, parameter_analysis):
+        # WHEN
+        result = parameter_analysis._normalize_names('parameter1')
+
+        # THEN EXPECT
+        assert result == ['parameter1']
+
+    def test_normalize_names_valid_list(self, parameter_analysis):
+        # WHEN
+        result = parameter_analysis._normalize_names(['parameter1', 'parameter2'])
+
+        # THEN EXPECT
+        assert result == ['parameter1', 'parameter2']
+
+    def test_get_xyweight_from_dataset_no_parameters_raises(self, parameter_analysis):
+        # WHEN
+        parameter_analysis.parameters = None
+
+        # THEN EXPECT
+        with pytest.raises(ValueError, match='No parameters Dataset provided'):
+            parameter_analysis._get_xyweight_from_dataset('parameter1')
+
+    def test_get_xyweight_from_dataset_wrong_parameter_name_raises(self, parameter_analysis):
+        # WHEN THEN EXPECT
+        with pytest.raises(
+            ValueError,
+            match="Parameter name 'nonexistent_parameter' not found in parameters Dataset",
+        ):
+            parameter_analysis._get_xyweight_from_dataset('nonexistent_parameter')
+
+    @pytest.mark.parametrize(
+        'non_finite_variance',
+        [np.inf, -np.inf, np.nan, -1.0, 0.0],
+        ids=['inf', '-inf', 'nan', 'negative', 'zero'],
+    )
+    def test_get_xyweight_from_dataset_non_finite_weights_raises(
+        self, parameter_analysis, non_finite_variance
+    ):
+        # WHEN
+        Q = sc.array(dims=['Q'], values=[0.1, 0.2])
+        parameter_analysis.parameters = sc.Dataset(
+            data={
+                'parameter1': sc.DataArray(
+                    data=sc.array(
+                        dims=['Q'],
+                        values=[1.0, 2.0],
+                        variances=[1.0, non_finite_variance],
+                        unit='meV',
+                    ),
+                    coords={'Q': Q},
+                ),
+            }
+        )
+
+        # THEN EXPECT
+        with pytest.raises(
+            ValueError, match="Non-finite variances found for parameter 'parameter1'"
+        ):
+            parameter_analysis._get_xyweight_from_dataset('parameter1')
+
+    def test_get_xyweight_from_dataset_valid(self, parameter_analysis):
+        # WHEN THEN
+        x, y, w = parameter_analysis._get_xyweight_from_dataset('parameter1')
+
+        # EXPECT
+        np.testing.assert_allclose(x, [0.1, 0.2])
+        np.testing.assert_allclose(y, [1.0, 2.0])
+        expected_w = 1 / np.sqrt([0.1, 0.2])
+        np.testing.assert_allclose(w, expected_w)
+
+    def test_repr(self, parameter_analysis):
+        # WHEN
+        repr_str = repr(parameter_analysis)
+
+        # THEN EXPECT
+        assert isinstance(repr_str, str)
+        assert 'ParameterAnalysis' in repr_str
+        assert f'display_name={parameter_analysis.display_name}' in repr_str
+        assert f'unique_name={parameter_analysis.unique_name}' in repr_str
+        assert f'n_parameters={len(parameter_analysis.parameters)}' in repr_str
+        assert 'parameter_names=' in repr_str
+        assert 'bindings=' in repr_str
diff --git a/tests/unit/easydynamics/convolution/test_numerical_convolution.py b/tests/unit/easydynamics/convolution/test_numerical_convolution.py
index 8b015355..7b6ada94 100644
--- a/tests/unit/easydynamics/convolution/test_numerical_convolution.py
+++ b/tests/unit/easydynamics/convolution/test_numerical_convolution.py
@@ -109,3 +109,127 @@ def test_convolution_with_temperature(
         )
 
         assert np.allclose(result, expected_result, rtol=1e-4)
+
+    @pytest.mark.parametrize(
+        'plan_valid, suppress_warnings, use_db, upsample',
+        [
+            (True, True, False, None),
+            (False, True, False, None),
+            (True, False, False, None),
+            (True, False, True, None),
+            (True, False, True, 10),
+            (False, False, True, 10),
+        ],
+        ids=[
+            'plan_valid=True, suppress_warnings=True, use_db=False, upsample=None',
+            'plan_valid=False, suppress_warnings=True, use_db=False, upsample=None',
+            'plan_valid=True, suppress_warnings=False, use_db=False, upsample=None',
+            'plan_valid=True, suppress_warnings=False, use_db=True, upsample=None',
+            'plan_valid=True, suppress_warnings=False, use_db=True, upsample=10',
+            'plan_valid=False, suppress_warnings=False, use_db=True, upsample=10',
+        ],
+    )
+    def test_convolution_branches(
+        self,
+        default_numerical_convolution,
+        monkeypatch,
+        plan_valid,
+        suppress_warnings,
+        use_db,
+        upsample,
+    ):
+        "Test that convolution branches are executed as expected based on settings."
+        # WHEN
+        conv = default_numerical_convolution
+
+        # --- Configure branches ---
+        conv.convolution_settings.suppress_warnings = suppress_warnings
+
+        conv.detailed_balance_settings.use_detailed_balance = use_db
+        conv.temperature = 10.0 if use_db else None
+
+        conv.upsample_factor = upsample
+
+        conv.convolution_settings.convolution_plan_is_valid = plan_valid
+
+        # --- Track calls ---
+        create_grid_called = False
+        check_width_calls = []
+
+        # Mock the methods that would be called in the branches to track
+        # whether they were called or not.
+        def fake_create_energy_grid():
+            nonlocal create_grid_called
+            create_grid_called = True
+            return conv._energy_grid
+
+        def fake_check_width_thresholds(*args, **kwargs):
+            check_width_calls.append((args, kwargs))
+
+        monkeypatch.setattr(conv, '_create_energy_grid', fake_create_energy_grid)
+        monkeypatch.setattr(conv, '_check_width_thresholds', fake_check_width_thresholds)
+
+        # --- Simplify numerics ---
+        dense = conv._energy_grid.energy_dense
+
+        monkeypatch.setattr(
+            conv.sample_components,
+            'evaluate',
+            lambda x: np.ones_like(dense),  # noqa: ARG005
+        )
+        monkeypatch.setattr(
+            conv.resolution_components,
+            'evaluate',
+            lambda x: np.ones_like(dense),  # noqa: ARG005
+        )
+
+        db_called = False
+
+        def fake_db(*args, **kwargs):  # noqa: ARG001
+            nonlocal db_called
+            db_called = True
+            return np.ones_like(dense)
+
+        monkeypatch.setattr(
+            'easydynamics.convolution.numerical_convolution.detailed_balance_factor',
+            fake_db,
+        )
+
+        monkeypatch.setattr(
+            'easydynamics.convolution.numerical_convolution.fftconvolve',
+            lambda a, b, mode: np.ones_like(dense),  # noqa: ARG005
+        )
+
+        interp_called = False
+
+        def fake_interp(*args, **kwargs):  # noqa: ARG001
+            nonlocal interp_called
+            interp_called = True
+            return np.ones_like(conv.energy.values)
+
+        monkeypatch.setattr(np, 'interp', fake_interp)
+
+        # THEN
+        result = conv.convolution()
+
+        # EXPECT
+        # Branch 1: energy grid recreation
+        assert create_grid_called is (not plan_valid)
+
+        # Branch 2: warnings
+        if suppress_warnings:
+            assert len(check_width_calls) == 0
+        else:
+            assert len(check_width_calls) == 2
+
+        # Branch 3: detailed balance
+        assert db_called is (use_db and conv.temperature is not None)
+
+        # Branch 4: interpolation
+        assert interp_called is (upsample is not None)
+
+        # Sanity: result shape
+        if upsample is not None:
+            assert result.shape == conv.energy.values.shape
+        else:
+            assert result.shape == dense.shape
diff --git a/tests/unit/easydynamics/sample_model/components/test_expression_component.py b/tests/unit/easydynamics/sample_model/components/test_expression_component.py
index 27399844..48fbafe2 100644
--- a/tests/unit/easydynamics/sample_model/components/test_expression_component.py
+++ b/tests/unit/easydynamics/sample_model/components/test_expression_component.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors
 # SPDX-License-Identifier: BSD-3-Clause
 
+from copy import copy
+
 import numpy as np
 import pytest
 from easyscience.variable import Parameter
@@ -34,6 +36,14 @@ def test_init_without_parameters(self):
         # EXPECT
         assert expr.A.value == pytest.approx(1.0)  # default
 
+    def test_init_with_parameter(self):
+        # WHEN THEN
+        A = Parameter('A', 3.0)
+        expr = ExpressionComponent('A * x', parameters={'A': A})
+
+        # EXPECT
+        assert expr.A.value == pytest.approx(3.0)
+
     def test_invalid_expression_raises(self):
         # WHEN THEN EXPECT
         with pytest.raises(ValueError, match='Invalid expression'):
@@ -172,3 +182,30 @@ def test_reserved_name_not_parameter(self):
 
         assert 'A' in names
         assert 'x' not in names  # x is reserved
+
+    def test_copy(self, expr: ExpressionComponent):
+        # WHEN THEN
+        expr_copy = copy(expr)
+
+        # EXPECT the copy is a new instance with the same properties
+        assert expr_copy is not expr
+        assert isinstance(expr_copy, ExpressionComponent)
+        assert expr_copy.expression == expr.expression
+        assert expr_copy.unit == expr.unit
+        assert expr_copy.display_name == expr.display_name
+
+        assert expr_copy.A.value == pytest.approx(expr.A.value)
+        assert expr_copy.x0.value == pytest.approx(expr.x0.value)
+        assert expr_copy.sigma.value == pytest.approx(expr.sigma.value)
+
+    def test_erf(self):
+        # WHEN
+        expr = ExpressionComponent('erf(x)')
+        x = np.array([-1.0, 0.0, 1.0])
+
+        # THEN
+        result = expr.evaluate(x)
+
+        # EXPECT
+        expected = np.array([-0.84270079, 0.0, 0.84270079])  # erf(-1), erf(0), erf(1)
+        np.testing.assert_allclose(result, expected, rtol=1e-5)
diff --git a/tests/unit/easydynamics/settings/test_convolution_settings.py b/tests/unit/easydynamics/settings/test_convolution_settings.py
index 60e70bdf..202f5bae 100644
--- a/tests/unit/easydynamics/settings/test_convolution_settings.py
+++ b/tests/unit/easydynamics/settings/test_convolution_settings.py
@@ -31,12 +31,14 @@ def test_init_with_custom_parameters(self):
         convolution_settings = ConvolutionSettings(
             upsample_factor=10,
             extension_factor=0.5,
+            suppress_warnings=True,
         )
 
         # THEN EXPECT
         assert convolution_settings.upsample_factor == 10
         assert convolution_settings.extension_factor == pytest.approx(0.5)
         assert convolution_settings.convolution_plan_is_valid is False
+        assert convolution_settings.suppress_warnings is True
 
     def test_init_with_None(self):
         """
@@ -53,6 +55,7 @@ def test_init_with_None(self):
         assert convolution_settings.upsample_factor is None
         assert convolution_settings.extension_factor is None
         assert convolution_settings.convolution_plan_is_valid is False
+        assert convolution_settings.suppress_warnings is False
 
     @pytest.mark.parametrize(
         'invalid_input, expected_exception, match',
@@ -62,6 +65,7 @@ def test_init_with_None(self):
             ({'upsample_factor': '5'}, TypeError, 'must be a numerical value or None'),
             ({'upsample_factor': 1.0}, ValueError, 'must be greater than 1'),
             ({'upsample_factor': 0.5}, ValueError, 'must be greater than 1'),
+            ({'suppress_warnings': 'yes'}, TypeError, r'must be True or False'),
         ],
         ids=[
             'extension_factor_not_numeric',
@@ -69,6 +73,7 @@ def test_init_with_None(self):
             'upsample_factor_not_numeric',
             'upsample_factor_equal_1',
             'upsample_factor_less_than_1',
+            'suppress_warnings_not_boolean',
         ],
     )
     def test_init_raises_for_invalid_input(self, invalid_input, expected_exception, match):
@@ -219,12 +224,28 @@ def test_convolution_plan_is_valid_setter_invalid(
         with pytest.raises(expected_exception, match=match):
             default_convolution_settings.convolution_plan_is_valid = value
 
+    def test_suppress_warnings_setter_valid(self, default_convolution_settings):
+        # WHEN
+        default_convolution_settings.suppress_warnings = True
+
+        # THEN EXPECT
+        assert default_convolution_settings.suppress_warnings is True
+
+    def test_suppress_warnings_setter_invalid(self, default_convolution_settings):
+        # WHEN / THEN / EXPECT
+        with pytest.raises(TypeError, match=r'must be True or False'):
+            default_convolution_settings.suppress_warnings = 'True'
+
     def test_repr_default(self, default_convolution_settings):
         # WHEN
         repr_str = repr(default_convolution_settings)
 
         # EXPECT
-        assert repr_str == ('ConvolutionSettings(upsample_factor=5.0, extension_factor=0.2)')
+        assert repr_str == (
+            f'ConvolutionSettings(upsample_factor={default_convolution_settings.upsample_factor}, '
+            f'extension_factor={default_convolution_settings.extension_factor}, '
+            f'suppress_warnings={default_convolution_settings.suppress_warnings})'
+        )
 
     def test_repr_reflects_updated_values(self, default_convolution_settings):
         # WHEN
@@ -234,4 +255,8 @@ def test_repr_reflects_updated_values(self, default_convolution_settings):
         repr_str = repr(default_convolution_settings)
 
         # EXPECT
-        assert repr_str == ('ConvolutionSettings(upsample_factor=3.0, extension_factor=0.5)')
+        assert repr_str == (
+            f'ConvolutionSettings(upsample_factor={default_convolution_settings.upsample_factor}, '
+            f'extension_factor={default_convolution_settings.extension_factor}, '
+            f'suppress_warnings={default_convolution_settings.suppress_warnings})'
+        )
diff --git a/tools/license_headers.py b/tools/license_headers.py
index 47d23524..f276ca1b 100644
--- a/tools/license_headers.py
+++ b/tools/license_headers.py
@@ -25,7 +25,9 @@
 
 
 def load_pyproject(repo_path: Union[str, Path]) -> dict[str, Any]:
-    """Load and return parsed ``pyproject.toml`` data for the repository."""
+    """
+    Load and return parsed ``pyproject.toml`` data for the repository.
+    """
     repo_root = find_repository_root(repo_path)
     pyproject_path = repo_root / 'pyproject.toml'
 
@@ -56,7 +58,9 @@ def get_exclude_patterns(
     exclude_values: list[str],
     exclude_from_pyproject_toml: Optional[str],
 ) -> list[str]:
-    """Return normalized exclude patterns from CLI and ``pyproject.toml``."""
+    """
+    Return normalized exclude patterns from CLI and ``pyproject.toml``.
+    """
     pyproject_data = load_pyproject(repo_path)
     patterns: list[str] = []
 
@@ -119,7 +123,9 @@ def get_file_creation_year(file_path: Union[str, Path]) -> str:
 
 
 def get_org_url(repo_path: Union[str, Path]) -> str:
-    """Return the organization URL derived from the repository source URL."""
+    """
+    Return the organization URL derived from the repository source URL.
+    """
     pyproject_data = load_pyproject(repo_path)
     repo_url = pyproject_data['project']['urls']['Source Code']
     return repo_url.rsplit('/', 1)[0]