Merge branch 'master' into dw/rand_multiple_consistent

Author: devmotion

Project.toml

@@ -1,7 +1,7 @@
 name = "Distributions"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
 authors = ["JuliaStats"]
-version = "0.25.113"
+version = "0.25.117"
 
 [deps]
 AliasTables = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"

docs/Project.toml

@@ -3,5 +3,5 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
 
 [compat]
-Documenter = "0.26, 0.27"
+Documenter = "1"
 GR = "0.72.1, 0.73"

docs/make.jl

@@ -1,7 +1,7 @@
 using Documenter, Distributions
 import Random: AbstractRNG, rand!
 
-makedocs(
+makedocs(;
     sitename = "Distributions.jl",
     modules  = [Distributions],
     format   = Documenter.HTML(; prettyurls = get(ENV, "CI", nothing) == "true"),
@@ -17,16 +17,18 @@ makedocs(
         "reshape.md",
         "cholesky.md",
         "mixture.md",
+        "product.md",
         "order_statistics.md",
         "convolution.md",
         "fit.md",
         "extends.md",
         "density_interface.md",
-    ]
+    ],
+    warnonly = true,
 )
 
 deploydocs(;
     repo = "github.com/JuliaStats/Distributions.jl.git",
     versions = ["stable" => "v^", "v#.#", "dev" => "master"],
-    push_preview=true,
+    push_preview = true,
 )

docs/src/multivariate.md

@@ -57,7 +57,6 @@ MvNormalCanon
 MvLogitNormal
 MvLogNormal
 Dirichlet
-Product
 ```
 
 ## Addition Methods
@@ -104,15 +103,6 @@ params{D<:Distributions.AbstractMvLogNormal}(::Type{D},m::AbstractVector,S::Abst
 Distributions._logpdf(d::MultivariateDistribution, x::AbstractArray)
 ```
 
-## Product distributions
-
-```@docs
-Distributions.product_distribution
-```
-
-Using `product_distribution` is advised to construct product distributions.
-For some distributions, it constructs a special multivariate type.
-
 ## Index
 
 ```@index

docs/src/product.md

@@ -0,0 +1,27 @@
+# Product Distributions
+
+Product distributions are joint distributions of multiple independent distributions.
+It is recommended to use `product_distribution` to construct product distributions.
+Depending on the type of the argument, it may construct a different distribution type.
+
+## Multivariate products
+
+```@docs
+Distributions.product_distribution(::AbstractArray{<:Distribution{<:ArrayLikeVariate}})
+Distributions.product_distribution(::AbstractVector{<:Normal})
+Distributions.ProductDistribution
+Distributions.Product
+```
+
+## NamedTuple-variate products
+
+```@docs
+Distributions.product_distribution(::NamedTuple{<:Any,<:Tuple{Distribution,Vararg{Distribution}}})
+Distributions.ProductNamedTupleDistribution
+```
+
+## Index
+
+```@index
+Pages = ["product.md"]
+```

src/Distributions.jl

@@ -41,6 +41,7 @@ export
     Multivariate,
     Matrixvariate,
     CholeskyVariate,
+    NamedTupleVariate,
     Discrete,
     Continuous,
     Sampleable,
@@ -296,6 +297,7 @@ include("univariates.jl")
 include("edgeworth.jl")
 include("multivariates.jl")
 include("matrixvariates.jl")
+include("namedtuple/productnamedtuple.jl")
 include("cholesky/lkjcholesky.jl")
 include("samplers.jl")
 

src/cholesky/lkjcholesky.jl

@@ -107,8 +107,13 @@ function insupport(d::LKJCholesky, R::LinearAlgebra.Cholesky)
     return true
 end
 
-function StatsBase.mode(d::LKJCholesky)
-    factors = Matrix{float(partype(d))}(LinearAlgebra.I, size(d))
+function mode(d::LKJCholesky; check_args::Bool=true)
+    @check_args(
+        LKJCholesky,
+        @setup(η = d.η),
+        (η, η > 1, "mode is defined only when η > 1."),
+    )
+    factors = Matrix{eltype(d)}(LinearAlgebra.I, size(d))
     return LinearAlgebra.Cholesky(factors, d.uplo, 0)
 end
 

src/common.jl

@@ -16,6 +16,12 @@ const Univariate    = ArrayLikeVariate{0}
 const Multivariate  = ArrayLikeVariate{1}
 const Matrixvariate = ArrayLikeVariate{2}
 
+"""
+`F <: NamedTupleVariate{K}` specifies that the variate or a sample is of type
+`NamedTuple{K}`.
+"""
+struct NamedTupleVariate{K} <: VariateForm end
+
 """
 `F <: CholeskyVariate` specifies that the variate or a sample is of type
 `LinearAlgebra.Cholesky`.

src/mixtures/mixturemodel.jl

@@ -1,19 +1,21 @@
 # finite mixture models
 
 """
+    AbstractMixtureModel <: Distribution
 
-  All subtypes of `AbstractMixtureModel` should implement the following methods:
+All subtypes of `AbstractMixtureModel` should implement the following methods:
 
-  - ncomponents(d): the number of components
+  - `ncomponents(d)`: the number of components
 
-  - component(d, k):  return the k-th component
+  - `component(d, k)`:  return the k-th component
 
-  - probs(d):       return a vector of prior probabilities over components.
+  - `probs(d)`:       return a vector of prior probabilities over components.
 """
 abstract type AbstractMixtureModel{VF<:VariateForm,VS<:ValueSupport,C<:Distribution} <: Distribution{VF, VS} end
 
 """
-MixtureModel{VF<:VariateForm,VS<:ValueSupport,C<:Distribution,CT<:Real}
+    MixtureModel{VF<:VariateForm,VS<:ValueSupport,C<:Distribution,CT<:Real}
+
 A mixture of distributions, parametrized on:
 * `VF,VS` variate and support
 * `C` distribution family of the mixture

src/multivariate/product.jl

@@ -10,6 +10,10 @@ An N dimensional `MultivariateDistribution` constructed from a vector of N indep
 ```julia
 Product(Uniform.(rand(10), 1)) # A 10-dimensional Product from 10 independent `Uniform` distributions.
 ```
+
+!!! note
+    `Product` is deprecated and will be removed in the next breaking release.
+    Use [`product_distribution`](@ref) instead.
 """
 struct Product{
     S<:ValueSupport,