SciML · ChrisRackauckas · Jan 8, 2026 · Jul 29, 2025 · Jul 29, 2025 · Jan 8, 2026
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -13,6 +13,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
 BenchmarkTools = "1"
@@ -28,3 +29,4 @@ StanSample = "7"
 StatsBase = "0.33, 0.34"
 StatsPlots = "0.15"
 TransformVariables = "0.8"
+Turing = "0.38, 0.39, 0.40"
diff --git a/docs/make.jl b/docs/make.jl
@@ -13,12 +13,7 @@ makedocs(
     clean = true,
     doctest = false,
     modules = [DiffEqBayes],
-    strict = [
-        :doctest,
-        :linkcheck,
-        :parse_error,
-        :example_block,        # Other available options are        # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
-    ],
+    warnonly = true,
     format = Documenter.HTML(
         assets = ["assets/favicon.ico"],
         canonical = "https://docs.sciml.ai/DiffEqBayes/stable/"

diff --git a/docs/src/assets/Project.toml b/docs/src/assets/Project.toml
@@ -1,6 +1,5 @@
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
-CmdStan = "593b3428-ca2f-500c-ae53-031589ec8ddd"
 DiffEqBayes = "ebbdde9d-f333-5424-9be2-dbf1e9acfb5e"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -9,22 +8,25 @@ OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 ParameterizedFunctions = "65888b18-ceab-5e60-b2b9-181511a3b968"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
+StanSample = "c1514b29-d3a0-5178-b312-660c88baa699"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
 BenchmarkTools = "1"
-CmdStan = "6"
 DiffEqBayes = "3"
 Distributions = "0.25"
-Documenter = "0.27"
+Documenter = "1"
 DynamicHMC = "3"
 OrdinaryDiffEq = "6"
 ParameterizedFunctions = "5"
 Plots = "1"
-RecursiveArrayTools = "2, 3"
+RecursiveArrayTools = "3"
+StanSample = "7"
 StatsBase = "0.33, 0.34"
 StatsPlots = "0.15"
 TransformVariables = "0.8"
+Turing = "0.38, 0.39, 0.40"
diff --git a/docs/src/examples.md b/docs/src/examples.md
@@ -38,7 +38,7 @@ data = convert(Array, randomized)
 ### Stan
 
 ```@example all
-using CmdStan #required for using the Stan backend
+using StanSample #required for using the Stan backend
 bayesian_result_stan = stan_inference(prob1, :rk45, t, data, priors)
 ```
 

diff --git a/docs/src/examples/pendulum.md b/docs/src/examples/pendulum.md
@@ -7,7 +7,7 @@ pendulum.
 
 ```@example pendulum
 using DiffEqBayes, OrdinaryDiffEq, RecursiveArrayTools, Distributions, Plots, StatsPlots,
-      BenchmarkTools, TransformVariables, CmdStan, DynamicHMC
+      BenchmarkTools, TransformVariables, StanSample, DynamicHMC
 ```
 
 Let's define our simple pendulum problem. Here, our pendulum has a drag term `ω`

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -2,7 +2,7 @@
 
 This repository is a set of extension functionality for estimating the parameters
 of differential equations using Bayesian methods. It allows the choice of using
-[CmdStan.jl](https://stanjulia.github.io/CmdStan.jl/stable/), [Turing.jl](https://turing.ml/stable/docs/using-turing/), [DynamicHMC.jl](https://www.tamaspapp.eu/DynamicHMC.jl/stable/) and
+[StanSample.jl](https://stanjulia.github.io/StanSample.jl/stable/), [Turing.jl](https://turing.ml/stable/docs/using-turing/), [DynamicHMC.jl](https://www.tamaspapp.eu/DynamicHMC.jl/stable/) and
 [ApproxBayes.jl](https://github.com/marcjwilliams1/ApproxBayes.jl) to perform a
 Bayesian estimation of a differential equation problem specified via the [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/) interface.
 
@@ -78,11 +78,12 @@ You can also download the
 ```
 
 ```@eval
-using TOML
+using TOML, Markdown
 version = TOML.parse(read("../../Project.toml", String))["version"]
 name = TOML.parse(read("../../Project.toml", String))["name"]
 link = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
        "/assets/Manifest.toml"
+Markdown.parse(link)
 ```
 
 ```@raw html
@@ -91,11 +92,12 @@ link = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
 ```
 
 ```@eval
-using TOML
+using TOML, Markdown
 version = TOML.parse(read("../../Project.toml", String))["version"]
 name = TOML.parse(read("../../Project.toml", String))["name"]
 link = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
        "/assets/Project.toml"
+Markdown.parse(link)
 ```
 
 ```@raw html

diff --git a/docs/src/methods.md b/docs/src/methods.md
@@ -17,9 +17,9 @@ stan_inference(prob::DiffEqBase.DEProblem, alg, t, data, priors = nothing;
     output_format = :mcmcchains, print_summary = true, tmpdir = mktempdir())
 ```
 
-`stan_inference` uses [Stan.jl](https://stanjulia.github.io/CmdStan.jl/latest/INTRO/)
+`stan_inference` uses [StanSample.jl](https://stanjulia.github.io/StanSample.jl/stable/)
 to perform the Bayesian inference. The
-[Stan installation process](https://stanjulia.github.io/CmdStan.jl/latest/INSTALLATION/)
+[Stan installation process](https://stanjulia.github.io/StanSample.jl/stable/INSTALLATION/)
 is required to use this function. Currently `CmdStan v2.34.1` is supported.
 
 `prob` can be any `DEProblem` with a corresponding `alg` choice. `alg` is a choice between `:rk45` and `:bdf`, the two internal integrators of Stan. `t` is the array of time and `data` is the array where the first dimension (columns) corresponds to the array of system values. `priors` is an array of prior distributions for each parameter, specified via a [Distributions.jl](https://juliastats.github.io/Distributions.jl/dev/) type. `likelihood` is the likelihood distribution to use with the arguments from `vars`, and `vars` is a tuple of priors for the distributions of the likelihood hyperparameters. The special value `StanODEData()` in this tuple denotes the position that the ODE solution takes in the likelihood's parameter list.
@@ -45,7 +45,7 @@ type.
 
 The `turing_inference` interacts with `SciML.CommonSolve.solve` and `StatsBase.sample`. Both accept many arguments depending on the solver and sampling algorithm.
 These arguments are supplied to `turing_inferene` function via `solve_kwargs`, `sample_args`, and `sample_kwargs` arguments. Please refer to [the `solve` documentation](https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/) for `solve_kwargs`, e.g. `solve_kwargs = Dict(:save_idxs => [1])`.
-The `solve` keyword arguments default to `save_idxs = nothing`. Similarly please refer to [the `sample` documentation]((https://turinglang.org/v0.26/docs/using-turing/guide#sampling-multiple-chains)) for `sample_args` and `sample_kwargs`. The four positional argument are as following: `sampler`, the sampling algorithm. Sampling from multiple chains is possible serially or parallelly using `parallel_type`. Third `num_samples`, the number of samples per MCMC chain and `n_chains`, the number of MCMC chains. The positional arguments default to the following values.
+The `solve` keyword arguments default to `save_idxs = nothing`. Similarly please refer to [the `sample` documentation](https://turinglang.org/v0.26/docs/using-turing/guide#sampling-multiple-chains) for `sample_args` and `sample_kwargs`. The four positional argument are as following: `sampler`, the sampling algorithm. Sampling from multiple chains is possible serially or parallelly using `parallel_type`. Third `num_samples`, the number of samples per MCMC chain and `n_chains`, the number of MCMC chains. The positional arguments default to the following values.
 
 ```julia
 sampler = Turing.NUTS(0.65)