Update stale links in docsrc/*.rst files

Author: amas0

docsrc/community.rst

@@ -24,7 +24,7 @@ Software
   with yearly, weekly, and daily seasonality, plus holiday effects.
 
 - `ArviZ <https://github.com/arviz-devs/arviz>`__ A Python package (with a
-  `Julia interface <https://julia.arviz.org/stable/>`_) for exploratory analysis of
+  `Julia interface <https://julia.arviz.org/ArviZ/stable/>`_) for exploratory analysis of
   Bayesian models. Includes functions for posterior analysis, data storage,
   model checking, comparison and diagnostics.
 

docsrc/users-guide/hello_world.rst

@@ -115,7 +115,7 @@ Accessing the results
 ^^^^^^^^^^^^^^^^^^^^^
 
 The sampler outputs are the set of per-chain
-`Stan CSV files <https://mc-stan.org/docs/cmdstan-guide/stan-csv.html>`__,
+`Stan CSV files <https://mc-stan.org/docs/cmdstan-guide/stan_csv_apdx.html>`__,
 a non-standard CSV file format.
 Each data row of the Stan CSV file contains the per-iteration estimate of the Stan model
 parameters, transformed parameters,  and generated quantities variables.
@@ -200,7 +200,7 @@ all model parameters and quantities of interest in a pandas.DataFrame:
 
 
 CmdStan is distributed with a second posterior analysis utility
-`diagnose <https://mc-stan.org/docs/cmdstan-guide/diagnose.html>`__
+`diagnose <https://mc-stan.org/docs/cmdstan-guide/diagnose_utility.html>`__
 which analyzes the per-draw sampler parameters across all chains
 looking for potential problems which indicate that the sample
 isn't a representative sample from the posterior.

docsrc/users-guide/outputs.rst

@@ -7,7 +7,7 @@ CSV File Outputs
 ----------------
 
 Underlyingly, the CmdStan outputs are a set of per-chain
-`Stan CSV files <https://mc-stan.org/docs/cmdstan-guide/stan-csv.html#mcmc-sampler-csv-output>`__.
+`Stan CSV files <https://mc-stan.org/docs/cmdstan-guide/stan_csv_apdx.html#mcmc-sampler-csv-output>`__.
 The filenames follow the template '<model_name>-<YYYYMMDDHHMMSS>-<chain_id>'
 plus the file suffix '.csv'.
 CmdStanPy also captures the per-chain console and error messages.

docsrc/users-guide/overview.rst

@@ -10,12 +10,12 @@ With CmdStanPy, you can:
 
 + Do inference on the model conditioned on the data, using one of Stan inference algorithms
 
-    + Exact Bayesian estimation using the `NUTS-HMC sampler <https://mc-stan.org/docs/reference-manual/hmc.html>`__.
+    + Exact Bayesian estimation using the `NUTS-HMC sampler <https://mc-stan.org/docs/reference-manual/mcmc.html#hamiltonian-monte-carlo>`__.
 
     + Approximate Bayesian estimation algorithms `Pathfinder <https://mc-stan.org/docs/reference-manual/pathfinder.html>`__
-      and `ADVI <https://mc-stan.org/docs/reference-manual/vi-algorithms.html>`__.
+      and `ADVI <https://mc-stan.org/docs/reference-manual/variational.html>`__.
 
-    + MAP estimation by `optimization <https://mc-stan.org/docs/reference-manual/optimization-algorithms.html>`__.
+    + MAP estimation by `optimization <https://mc-stan.org/docs/reference-manual/optimization.html>`__.
 
 + Generate new quantities of interest from a model given an existing sample.
 

docsrc/users-guide/workflow.rst

@@ -129,7 +129,7 @@ For each CmdStan inference method, there is a corresponding method on the :class
 An example of each is provided in the `next section <examples.rst>`__.
 
 * The :meth:`~CmdStanModel.sample` method runs Stan's
-  `HMC-NUTS sampler <https://mc-stan.org/docs/reference-manual/hamiltonian-monte-carlo.html>`__.
+  `HMC-NUTS sampler <https://mc-stan.org/docs/reference-manual/mcmc.html#hamiltonian-monte-carlo>`__.
 
   It returns a :class:`CmdStanMCMC` object which contains
   a sample from the posterior distribution of the model conditioned on the data.
@@ -141,12 +141,12 @@ An example of each is provided in the `next section <examples.rst>`__.
   a sample from a Gaussian approximation the posterior distribution.
 
 * The :meth:`~CmdStanModel.variational` method runs Stan's
-  `Automatic Differentiation Variational Inference (ADVI) algorithm <https://mc-stan.org/docs/reference-manual/vi-algorithms-chapter.html>`__.
+  `Automatic Differentiation Variational Inference (ADVI) algorithm <https://mc-stan.org/docs/reference-manual/variational.html>`__.
 
   It returns a :class:`CmdStanVB` object which contains an approximation the posterior distribution.
 
 * The :meth:`~CmdStanModel.optimize` runs one of Stan's
-  `optimization algorithms <https://mc-stan.org/docs/reference-manual/optimization-algorithms-chapter.html>`__
+  `optimization algorithms <https://mc-stan.org/docs/reference-manual/optimization.html>`__
   to find a mode of the density specified by the Stan program.
 
   It returns a :class:`CmdStanMLE` object.
@@ -155,7 +155,7 @@ An example of each is provided in the `next section <examples.rst>`__.
   `generate_quantities method <https://mc-stan.org/docs/cmdstan-guide/standalone-generate-quantities.html>`__.
   which generates additional quantities of interest from a mode. Its take an existing fit as input and
   uses the parameter estimates in the fit to run the Stan program's
-  `generated quantities block <https://mc-stan.org/docs/reference-manual/program-block-generated-quantities.html>`__.
+  `generated quantities block <https://mc-stan.org/docs/reference-manual/blocks.html#program-block-generated-quantities>`__.
 
   It returns a :class:`CmdStanGQ` object.