ENH: add linkchecker

.github/workflows/linkcheck.yml

@@ -0,0 +1,44 @@
+name: Link Checker [Anaconda, Linux]
+on:
+  pull_request:
+    types: [opened, reopened]
+  schedule:
+    # UTC 12:00 is early morning in Australia
+    - cron:  '0 12 * * *'
+jobs:
+  link-check-linux:
+    name: Link Checking (${{ matrix.python-version }}, ${{ matrix.os }})
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: ["ubuntu-latest"]
+        python-version: ["3.12"]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Setup Anaconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-update-conda: true
+          auto-activate-base: true
+          miniconda-version: 'latest'
+          python-version: '3.12'
+          environment-file: environment.yml
+          activate-environment: quantecon
+      - name: Download "build" folder (cache)
+        uses: dawidd6/action-download-artifact@v7
+        with:
+          workflow: cache.yml
+          branch: main
+          name: build-cache
+          path: _build
+      - name: Link Checker
+        shell: bash -l {0}
+        run: jb build lectures --path-output=./ --builder=custom --custom-builder=linkcheck
+      - name: Upload Link Checker Reports
+        uses: actions/upload-artifact@v4
+        if: failure()
+        with:
+          name: linkcheck-reports
+          path: _build/linkcheck

lectures/kolmogorov_fwd.md

@@ -92,7 +92,7 @@ $$
 where distributions are understood as row vectors.
 
 Here's a visualization for the case $S = \{0, 1, 2\}$, so that $\dD$ is the [standard
-simplex](https://en.wikipedia.org/wiki/Simplex#The_standard_simplex) in $\RR^3$.
+simplex](https://en.wikipedia.org/wiki/Simplex) in $\RR^3$.
 
 The initial condition is `` (0, 0, 1)`` and the Markov matrix is
 

lectures/markov_prop.md

@@ -129,7 +129,7 @@ In addition to connecting probabilities to the Markov matrix,
 {eq}`markovpropd` says that the process depends on its history only through
 the current state.
 
-We [recall that](https://python.quantecon.org/finite_markov.html#Marginal-Distributions), if $X_t$
+We [recall that](https://python.quantecon.org/finite_markov.html#marginal-distributions), if $X_t$
 has distribution $\phi$, then $X_{t+1}$ has distribution $\phi P$.
 
 Since $\phi$ is understood as a row vector, the meaning is