diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index c7a374d2..3be3162c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -9,8 +9,6 @@ on:
 
 env:
   PYTHON: "Conda" # use Julia's packaged Conda build for installing packages
-  CMDSTAN_VERSION: "2.31.0"
-  CMDSTAN_PATH: "${{ GITHUB.WORKSPACE }}/.cmdstan/"
 
 jobs:
   test:
@@ -41,23 +39,11 @@ jobs:
             ${{ runner.os }}-test-${{ env.cache-name }}-
             ${{ runner.os }}-test-
             ${{ runner.os }}-
-      - name: Cache CmdStan
-        id: cache-cmdstan
-        uses: actions/cache@v2
-        with:
-          path: ${{ env.CMDSTAN_PATH }}
-          key: cmdstan-${{ env.CMDSTAN_VERSION }}-${{ runner.os }}
       - name: Install wget for windows
         if: matrix.os == 'windows-latest'
         uses: crazy-max/ghaction-chocolatey@v2
         with:
           args: install wget
-      - name: Download and build CmdStan
-        if: steps.cache-cmdstan.outputs.cache-hit != 'true'
-        run: |
-          wget -P ${{ env.CMDSTAN_PATH }} https://github.com/stan-dev/cmdstan/releases/download/v${{ env.CMDSTAN_VERSION }}/cmdstan-${{ env.CMDSTAN_VERSION }}.tar.gz
-          tar -xzpf ${{ env.CMDSTAN_PATH }}/cmdstan-${{ env.CMDSTAN_VERSION }}.tar.gz -C ${{ env.CMDSTAN_PATH }}
-          make -C ${{ env.CMDSTAN_PATH }}/cmdstan-${{ env.CMDSTAN_VERSION }}/ build
       - uses: julia-actions/julia-buildpkg@latest
       - name: Install ArviZ dependencies
         run: |
@@ -68,8 +54,6 @@ jobs:
           ArviZ.initialize_pandas()
         shell: julia --color=yes --project {0}
       - uses: julia-actions/julia-runtest@latest
-        env:
-          JULIA_CMDSTAN_HOME: ${{ env.CMDSTAN_PATH }}/cmdstan-${{ env.CMDSTAN_VERSION }}/ # required by CmdStan.jl
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v1
         with:
diff --git a/.github/workflows/futures.yml b/.github/workflows/futures.yml
index 841f38a2..d8f438a9 100644
--- a/.github/workflows/futures.yml
+++ b/.github/workflows/futures.yml
@@ -9,8 +9,6 @@ on:
 
 env:
   PYTHON: "Conda" # use Julia's packaged Conda build for installing packages
-  CMDSTAN_VERSION: "2.25.0"
-  CMDSTAN_PATH: "${{ GITHUB.WORKSPACE }}/.cmdstan/"
 
 jobs:
   test:
@@ -43,19 +41,6 @@ jobs:
             ${{ runner.os }}-test-${{ env.cache-name }}-
             ${{ runner.os }}-test-
             ${{ runner.os }}-
-      - name: Cache CmdStan
-        id: cache-cmdstan
-        uses: actions/cache@v2
-        with:
-          path: ${{ env.CMDSTAN_PATH }}
-          key: cmdstan-${{ env.CMDSTAN_VERSION }}-${{ runner.os }}
-      - name: Download and build CmdStan
-        if: steps.cache-cmdstan.outputs.cache-hit != 'true'
-        run: |
-          wget -P ${{ env.CMDSTAN_PATH }} https://github.com/stan-dev/cmdstan/releases/download/v${{ env.CMDSTAN_VERSION }}/cmdstan-${{ env.CMDSTAN_VERSION }}.tar.gz
-          tar -xzpf ${{ env.CMDSTAN_PATH }}/cmdstan-${{ env.CMDSTAN_VERSION }}.tar.gz -C ${{ env.CMDSTAN_PATH }}
-          make -C ${{ env.CMDSTAN_PATH }}/cmdstan-${{ env.CMDSTAN_VERSION }}/ build
-        shell: bash
       - uses: julia-actions/julia-buildpkg@latest
         if: matrix.arviz_version != 'main' || github.event_name == 'create' && startsWith(github.ref, 'refs/tags/v')
       - name: "Install arviz#main"
@@ -79,5 +64,3 @@ jobs:
           ArviZ.initialize_pandas()
         shell: julia --color=yes --project {0}
       - uses: julia-actions/julia-runtest@latest
-        env:
-          JULIA_CMDSTAN_HOME: ${{ env.CMDSTAN_PATH }}/cmdstan-${{ env.CMDSTAN_VERSION }}/ # required by CmdStan.jl
diff --git a/docs/make.jl b/docs/make.jl
index df322f95..d20b65a7 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -40,15 +40,24 @@ doctestfilters = [
     r"\s+\"created_at\" => .*",  # ignore timestamps in doctests
 ]
 
+modules = [
+    ArviZ,
+    ArviZExampleData,
+    InferenceObjects,
+    InferenceObjectsNetCDF,
+    MCMCDiagnosticTools,
+    PSIS,
+]
+if isdefined(Base, :get_extension)
+    # using Requires, these docstrings are automatically loaded, but as an extension we need
+    # to manually specify the module
+    push!(
+        modules, Base.get_extension(InferenceObjects, :InferenceObjectsMCMCDiagnosticToolsExt)
+    )
+end
+
 makedocs(;
-    modules=[
-        ArviZ,
-        ArviZExampleData,
-        InferenceObjects,
-        InferenceObjectsNetCDF,
-        MCMCDiagnosticTools,
-        PSIS,
-    ],
+    modules,
     sitename="ArviZ.jl",
     pages=[
         "Home" => "index.md",
@@ -71,7 +80,7 @@ makedocs(;
         sidebar_sitename=false,
         canonical="stable",
     ),
-    doctestfilters=doctestfilters,
+    doctestfilters,
     linkcheck=true,
     analytics="G-W1G68W77YV",
 )
diff --git a/docs/src/api/data.md b/docs/src/api/data.md
index b4e7cfee..7e75d124 100644
--- a/docs/src/api/data.md
+++ b/docs/src/api/data.md
@@ -7,7 +7,6 @@ Pages = ["data.md"]
 ## Inference library converters
 
 ```@docs
-from_cmdstan
 from_mcmcchains
 from_samplechains
 ```
@@ -15,18 +14,10 @@ from_samplechains
 ## IO / Conversion
 
 ```@docs
-from_json
 from_netcdf
 to_netcdf
 ```
 
-## General functions
-
-```@docs
-concat
-extract
-```
-
 ## Example data
 
 ```@docs
diff --git a/docs/src/api/diagnostics.md b/docs/src/api/diagnostics.md
index 14b2bd2d..a7dbec71 100644
--- a/docs/src/api/diagnostics.md
+++ b/docs/src/api/diagnostics.md
@@ -4,15 +4,18 @@
 Pages = ["diagnostics.md"]
 ```
 
-## Reference
+## [Bayesian fraction of missing information](@id bfmi)
 
 ```@docs
 MCMCDiagnosticTools.bfmi
+```
+
+## [Effective sample size and $\widehat{R}$ diagnostic](@id ess_rhat)
+
+```@docs
 MCMCDiagnosticTools.ess
-MCMCDiagnosticTools.ess_rhat
-MCMCDiagnosticTools.mcse
 MCMCDiagnosticTools.rhat
-MCMCDiagnosticTools.rstar
+MCMCDiagnosticTools.ess_rhat
 ```
 
 The following autocovariance methods are supported:
@@ -22,3 +25,15 @@ MCMCDiagnosticTools.AutocovMethod
 MCMCDiagnosticTools.FFTAutocovMethod
 MCMCDiagnosticTools.BDAAutocovMethod
 ```
+
+## [Monte Carlo standard error](@id mcse)
+
+```@docs
+MCMCDiagnosticTools.mcse
+```
+
+## [$R^*$ diagnostic](@id rstar)
+
+```@docs
+MCMCDiagnosticTools.rstar
+```
diff --git a/docs/src/working_with_inference_data.md b/docs/src/working_with_inference_data.md
index 9b08143a..054b431c 100644
--- a/docs/src/working_with_inference_data.md
+++ b/docs/src/working_with_inference_data.md
@@ -221,7 +221,7 @@ school_bis_idx = ["Deerfield", "Choate", "Lawrenceville"]
 theta_school_diff[school=At(school_idx), school_bis=At(school_bis_idx)]
 ```
 
-## Add new chains using `concat`
+## Add new chains using `cat`
 
 Suppose after checking the `mcse` and realizing you need more samples, you rerun the model with two chains and obtain an `idata_rerun` object.
 
@@ -229,8 +229,8 @@ Suppose after checking the `mcse` and realizing you need more samples, you rerun
 idata_rerun = InferenceData(; posterior=set(post[chain=At([0, 1])]; chain=[4, 5]))
 ```
 
-You can combine the two using [`concat`](@ref).
+You can combine the two using [`cat`](@ref).
 
 ```@example wwid
-concat(idata[[:posterior]], idata_rerun; dim=:chain)
+cat(idata[[:posterior]], idata_rerun; dims=:chain)
 ```
diff --git a/src/ArviZ.jl b/src/ArviZ.jl
index 81eccefd..eeb03c60 100644
--- a/src/ArviZ.jl
+++ b/src/ArviZ.jl
@@ -93,9 +93,6 @@ export summarystats, compare, hdi, loo, loo_pit, r2_score, waic
 export MCMCDiagnosticTools, AutocovMethod, FFTAutocovMethod, BDAAutocovMethod
 export bfmi, ess, ess_rhat, mcse, rhat, rstar
 
-## Stats utils
-export autocov, autocorr, make_ufunc, wrap_xarray_ufunc
-
 ## InferenceObjects
 export InferenceObjects,
     Dataset,
@@ -113,7 +110,7 @@ export InferenceObjectsNetCDF, from_netcdf, to_netcdf
 export ArviZExampleData, describe_example_data, load_example_data
 
 ## Data
-export extract, from_json, from_cmdstan, from_mcmcchains, from_samplechains, concat
+export from_mcmcchains, from_samplechains
 
 ## rcParams
 export rcParams, with_rc_context
@@ -149,9 +146,7 @@ end
 include("utils.jl")
 include("rcparams.jl")
 include("xarray.jl")
-include("data.jl")
 include("plots.jl")
 include("stats.jl")
-include("stats_utils.jl")
 
 end # module
diff --git a/src/data.jl b/src/data.jl
deleted file mode 100644
index e52c78fb..00000000
--- a/src/data.jl
+++ /dev/null
@@ -1,23 +0,0 @@
-@forwardfun extract
-convert_result(::typeof(extract), result, args...) = convert(Dataset, result)
-
-function convert_to_inference_data(filename::AbstractString; kwargs...)
-    return from_netcdf(filename)
-end
-
-@forwardfun from_json
-@forwardfun from_beanmachine
-@forwardfun from_cmdstan
-@forwardfun from_cmdstanpy
-@forwardfun from_emcee
-@forwardfun from_pyro
-@forwardfun from_numpyro
-@forwardfun from_pystan
-
-@doc forwarddoc(:concat) concat
-
-function concat(data::InferenceData...; kwargs...)
-    return arviz.concat(data...; inplace=false, kwargs...)
-end
-
-Docs.getdoc(::typeof(concat)) = forwardgetdoc(:concat)
diff --git a/src/mcmcchains.jl b/src/mcmcchains.jl
index 828b7f38..231fd098 100644
--- a/src/mcmcchains.jl
+++ b/src/mcmcchains.jl
@@ -269,12 +269,3 @@ function from_mcmcchains(
 
     return all_idata
 end
-
-"""
-    from_cmdstan(posterior::Chains; kwargs...) -> InferenceData
-
-Call [`from_mcmcchains`](@ref) on output of `CmdStan`.
-"""
-function from_cmdstan(posterior::Chains; kwargs...)
-    return from_mcmcchains(posterior; library="CmdStan", kwargs...)
-end
diff --git a/src/stats_utils.jl b/src/stats_utils.jl
deleted file mode 100644
index 7c665df0..00000000
--- a/src/stats_utils.jl
+++ /dev/null
@@ -1,4 +0,0 @@
-@forwardfun autocov
-@forwardfun autocorr
-@forwardfun make_ufunc
-@forwardfun wrap_xarray_ufunc
diff --git a/test/Project.toml b/test/Project.toml
index cfe7d683..c7478f50 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -9,7 +9,6 @@ PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SampleChains = "754583d1-7fc4-4dab-93b5-5eaca5c9622e"
 SampleChainsDynamicHMC = "6d9fd711-e8b2-4778-9c70-c1dfb499d4c4"
-StanSample = "c1514b29-d3a0-5178-b312-660c88baa699"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
@@ -23,4 +22,3 @@ PyCall = "1.91.2"
 PyPlot = "2.8.2"
 SampleChains = "0.5"
 SampleChainsDynamicHMC = "0.3"
-StanSample = "6, 7"
diff --git a/test/runtests.jl b/test/runtests.jl
index 49eac165..d3378cdd 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -5,7 +5,6 @@ using Test
     include("helpers.jl")
     include("test_rcparams.jl")
     include("test_utils.jl")
-    include("test_data.jl")
     include("test_stats.jl")
     include("test_plots.jl")
     include("test_samplechains.jl")
diff --git a/test/test_data.jl b/test/test_data.jl
deleted file mode 100644
index b4506149..00000000
--- a/test/test_data.jl
+++ /dev/null
@@ -1,32 +0,0 @@
-using ArviZ, DimensionalData, Test
-
-@testset "extract" begin
-    idata = random_data()
-    post = extract(idata, :posterior; combined=false)
-    for k in keys(idata.posterior)
-        @test haskey(post, k)
-        @test post[k] ≈ idata.posterior[k]
-        dims = DimensionalData.dims(post)
-        dims_exp = DimensionalData.dims(idata.posterior)
-        @test DimensionalData.name(dims) === DimensionalData.name(dims_exp)
-        @test DimensionalData.index(dims) == DimensionalData.index(dims_exp)
-    end
-    prior = extract(idata, :prior; combined=false)
-    for k in keys(idata.prior)
-        @test haskey(prior, k)
-        @test prior[k] ≈ idata.prior[k]
-        dims = DimensionalData.dims(prior)
-        dims_exp = DimensionalData.dims(idata.prior)
-        @test DimensionalData.name(dims) === DimensionalData.name(dims_exp)
-        @test DimensionalData.index(dims) == DimensionalData.index(dims_exp)
-    end
-end
-
-@testset "concat" begin
-    data = random_data()
-    idata1 = InferenceData(; posterior=data.posterior)
-    idata2 = InferenceData(; prior=data.prior)
-    new_idata1 = concat(idata1, idata2)
-    new_idata2 = InferenceData(; posterior=data.posterior, prior=data.prior)
-    test_idata_approx_equal(new_idata1, new_idata2)
-end
diff --git a/test/test_mcmcchains.jl b/test/test_mcmcchains.jl
index 030469b3..17579992 100644
--- a/test/test_mcmcchains.jl
+++ b/test/test_mcmcchains.jl
@@ -2,39 +2,7 @@ using Test
 using ArviZ
 using DimensionalData
 using MCMCChains: MCMCChains
-using OrderedCollections, StanSample
-
-const noncentered_schools_stan_model = """
-    data {
-        int<lower=0> J;
-        real y[J];
-        real<lower=0> sigma[J];
-    }
-    parameters {
-        real mu;
-        real<lower=0> tau;
-        real eta[J];
-    }
-    transformed parameters {
-        real theta[J];
-        for (j in 1:J)
-            theta[j] = mu + tau * eta[j];
-    }
-    model {
-        mu ~ normal(0, 5);
-        tau ~ cauchy(0, 5);
-        eta ~ normal(0, 1);
-        y ~ normal(theta, sigma);
-    }
-    generated quantities {
-        vector[J] log_lik;
-        vector[J] y_hat;
-        for (j in 1:J) {
-            log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);
-            y_hat[j] = normal_rng(theta[j], sigma[j]);
-        }
-    }
-"""
+using OrderedCollections
 
 function makechains(
     names, ndraws, nchains, domains=[Float64 for _ in names]; seed=42, internal_names=[]
@@ -52,17 +20,6 @@ function makechains(nvars::Int, args...; kwargs...)
     return makechains(names, args...; kwargs...)
 end
 
-function stan_noncentered_schools(data, draws, chains; tmpdir=mktempdir())
-    model_name = "school8"
-    stan_model = SampleModel(model_name, noncentered_schools_stan_model, tmpdir)
-    _ = stan_sample(
-        stan_model; data=data, num_chains=chains, num_samples=draws, summary=false
-    )
-    chns = read_samples(stan_model, :mcmcchains; include_internals=true)
-    outfiles = ["$(stan_model.output_base)_chain_$(i).csv" for i in 1:chains]
-    return (model=stan_model, files=outfiles, chains=chns)
-end
-
 function test_chains_data(chns, idata, group, names=names(chns); coords=(;), dims=(;))
     ndraws, nvars, nchains = size(chns)
     @test idata isa InferenceData
@@ -336,48 +293,3 @@ end
 
     @test ArviZ.summary(chn) !== nothing
 end
-
-Sys.iswindows() || VERSION < v"1.8" ||  @testset "from_cmdstan" begin
-    data = noncentered_schools_data()
-    mktempdir() do path
-        output = stan_noncentered_schools(data, 500, 4; tmpdir=path)
-        posterior_predictive = prior_predictive = [:y_hat]
-        log_likelihood = :log_lik
-        coords = (school=1:8,)
-        dims = (
-            theta=[:school], y=[:school], log_lik=[:school], y_hat=[:school], eta=[:school]
-        )
-        idata1 = from_cmdstan(
-            output.chains;
-            posterior_predictive,
-            log_likelihood,
-            prior=output.chains,
-            prior_predictive,
-            coords,
-            dims,
-        )
-        idata2 = from_cmdstan(
-            output.files;
-            posterior_predictive,
-            log_likelihood,
-            prior=output.files,
-            prior_predictive,
-            coords=Dict(pairs(coords)),
-            dims=Dict(pairs(dims)),
-        )
-        @testset "idata.$(group)" for group in ArviZ.groupnames(idata2)
-            ds1 = idata1[group]
-            ds2 = idata2[group]
-
-            for var_name in keys(ds1)
-                da1 = ds1[var_name]
-                da2 = ds2[var_name]
-                if ndims(da1) == 3
-                    @test da1 ≈ permutedims(da2, (:draw, :chain, :school))
-                else
-                    @test da1 ≈ permutedims(da2, (:draw, :chain))
-                end
-            end
-        end
-    end
-end