Fix failing tests

Project.toml

@@ -3,7 +3,7 @@ uuid = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 keywords = ["markov chain monte carlo", "probablistic programming"]
 license = "MIT"
 desc = "Chain types and utility functions for MCMC simulations."
-version = "4.7.3"
+version = "4.7.4"
 
 [deps]
 AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"

docs/make.jl

@@ -28,7 +28,13 @@ makedocs(
     format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
     modules = [MCMCChains],
     strict = true,
-    checkdocs = :exports
+    checkdocs = :exports,
+    # https://github.com/JuliaLang/julia/pull/37085#issuecomment-683356098
+    doctestfilters = [
+        r"{([a-zA-Z0-9]+,\s?)+[a-zA-Z0-9]+}",
+        r"(Array{[a-zA-Z0-9]+,\s?1}|Vector{[a-zA-Z0-9]+})",
+        r"(Array{[a-zA-Z0-9]+,\s?2}|Matrix{[a-zA-Z0-9]+})",
+    ]
 )
 
 deploydocs(repo = "github.com/TuringLang/MCMCChains.jl.git", push_preview=true)

src/chains.jl

@@ -77,14 +77,14 @@ Return a new chain with only a specific `section` or multiple `sections` pulled
 julia> chn = Chains(rand(100, 2, 1), [:a, :b], Dict(:internals => [:a]));
 
 julia> names(chn)
-2-element Array{Symbol,1}:
+2-element Vector{Symbol}:
  :a
  :b
 
 julia> chn2 = Chains(chn, :internals);
 
 julia> names(chn2)
-1-element Array{Symbol,1}:
+1-element Vector{Symbol}:
  :a
 ```
 """
@@ -122,7 +122,7 @@ If the chain contains a parameter of name `:sym` it will be returned as well.
 julia> chn = Chains(rand(100, 2, 2), ["A[1]", "A[2]"]);
 
 julia> namesingroup(chn, :A)
-2-element Array{Symbol,1}:
+2-element Vector{Symbol}:
  Symbol("A[1]")
  Symbol("A[2]")
 ```
@@ -192,14 +192,15 @@ names as the values.
 Passing `flatten=true` will return a `NamedTuple` with keys ungrouped.
 
 # Example
-```julia
-julia> chn = Chains([1:5 6:10]);
+
+```jldoctest
+julia> chn = Chains([1:2 3:4]);
 
 julia> get(chn, :param_1)
-(param_1 = [1; 2; … ; 4; 5],)
+(param_1 = [1; 2],)
 
-julia> get(chn, [:param_1, :param_2])
-(param_2 = [6; 7; … ; 9; 10], param_1 = [1; 2; … ; 4; 5])
+julia> get(chn, [:param_2])
+(param_2 = [3; 4],)
 
 julia> get(chn, :param_1; flatten=true)
 (param_1 = 1,)
@@ -239,14 +240,15 @@ Return all parameters in a given section(s) as a `NamedTuple`.
 Passing `flatten=true` will return a `NamedTuple` with keys ungrouped.
 
 # Example
-```julia
-julia> chn = Chains([1:5 6:10], [:a, :b], Dict(:internals => [:a]));
 
-julia> x = get(chn; section=:parameters)
-(b = [6; 7; … ; 9; 10],)
+```jldoctest
+julia> chn = Chains([1:2 3:4], [:a, :b], Dict(:internals => [:a]));
 
-julia> x = get(chn; section=[:internals, :parameters])
-(a = [1; 2; … ; 4; 5], b = [6; 7; … ; 9; 10])
+julia> get(chn; section=:parameters)
+(b = [3; 4],)
+
+julia> get(chn; section=[:internals])
+(a = [1; 2],)
 ```
 """
 function Base.get(
@@ -284,7 +286,8 @@ in their name (as in "P[1]") will be grouped into the returned value as P.
 Passing `flatten=true` will return a `NamedTuple` with keys ungrouped.
 
 # Example
-```julia
+
+```jldoctest
 julia> chn = Chains(1:5);
 
 julia> x = get_params(chn);
@@ -293,7 +296,7 @@ julia> x.param_1
 2-dimensional AxisArray{Int64,2,...} with axes:
     :iter, 1:1:5
     :chain, 1:1
-And data, a 5×1 Array{Int64,2}:
+And data, a 5×1 Matrix{Int64}:
  1
  2
  3
@@ -690,14 +693,14 @@ julia> chn = Chains(rand(100, 2, 2), ["one", "two"]);
 julia> chn2 = replacenames(chn, "one" => "A");
 
 julia> names(chn2)
-2-element Array{Symbol,1}:
+2-element Vector{Symbol}:
  :A
  :two
 
 julia> chn3 = replacenames(chn2, Dict("A" => "one", "two" => "B"));
 
 julia> names(chn3) 
-2-element Array{Symbol,1}:
+2-element Vector{Symbol}:
  :one
  :B
 ```

src/gelmandiag.jl

@@ -103,8 +103,8 @@ function gelmandiag(
     results = gelmandiag(psi; alpha = alpha, kwargs...)
 
     # Create a named tuple with the results.
-    colnames = (:psrf, Symbol(100 * (1 - alpha / 2), :%))
-    nt = (; parameters = names(_chains), zip(colnames, results)...)
+    sym = Symbol(100 * (1 - alpha / 2), :%)
+    nt = (; :parameters => names(_chains), :psrf => results.psrf, sym => results.psrfci)
 
     return ChainDataFrame("Gelman, Rubin, and Brooks Diagnostic", nt)
 end
@@ -124,8 +124,8 @@ function gelmandiag_multivariate(
     results = gelmandiag_multivariate(psi; alpha = alpha, kwargs...)
 
     # Create a named tuple with the results.
-    colnames = (:psrf, Symbol(100 * (1 - alpha / 2), :%))
-    nt = (; parameters = names(_chains), zip(colnames, (results.psrf, results.psrfci))...)
+    sym = Symbol(100 * (1 - alpha / 2), :%)
+    nt = (; :parameters => names(_chains), :psrf => results.psrf, sym => results.psrfci)
 
     return ChainDataFrame("Gelman, Rubin, and Brooks Diagnostic", nt),
         results.multivariate

src/stats.jl

@@ -112,7 +112,7 @@ end
 
 function chaindataframe_changerate(name, names_of_params, chains; kwargs...)
     # Compute the change rates.
-    changerates, mvchangerate = chains
+    changerates, mvchangerate = changerate(chains)
 
     # Summarize the results in a named tuple.
     nt = (; zip(names_of_params, changerates)..., multivariate = mvchangerate)

test/runtests.jl

@@ -32,7 +32,16 @@ Random.seed!(0)
             :(using MCMCChains);
             recursive=true
         )
-        doctest(MCMCChains)
+
+        doctest(
+            MCMCChains;
+            # https://github.com/JuliaLang/julia/pull/37085#issuecomment-683356098
+            doctestfilters = [
+                r"{([a-zA-Z0-9]+,\s?)+[a-zA-Z0-9]+}",
+                r"(Array{[a-zA-Z0-9]+,\s?1}|Vector{[a-zA-Z0-9]+})",
+                r"(Array{[a-zA-Z0-9]+,\s?2}|Matrix{[a-zA-Z0-9]+})",
+            ],
+        )
     end
 
     # run tests for effective sample size