Merge branch 'master' of github.com:JuliaStats/MixedModels.jl into gl…

…mm_dispersion_deviance

.github/workflows/CompatHelper.yml

@@ -1,24 +1,16 @@
 name: CompatHelper
-
 on:
   schedule:
-    - cron: '43 7 * * *'
-
+    - cron: 43 7 * * *
+  workflow_dispatch:
 jobs:
   CompatHelper:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        julia-version: [1.2.0]
-        julia-arch: [x86]
-        os: [ubuntu-latest]
+    runs-on: ubuntu-latest
     steps:
-      - uses: julia-actions/setup-julia@latest
-        with:
-          version: ${{ matrix.julia-version }}
       - name: Pkg.add("CompatHelper")
         run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
       - name: CompatHelper.main()
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: julia -e 'using CompatHelper; CompatHelper.main()'
+          COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
+        run: julia -e 'using CompatHelper; CompatHelper.main()'

.github/workflows/Future.yml

@@ -6,9 +6,6 @@ on:
     paths-ignore:
       - 'LICENSE.md'
       - 'README.md'
-  release:
-    types:
-      - created
 jobs:
   ci:
     runs-on: ${{ matrix.os }}

.github/workflows/TagBot.yml

@@ -2,11 +2,12 @@ name: TagBot
 on:
   schedule:
     - cron: 5 * * * *
+  workflow_dispatch:
 jobs:
   TagBot:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v1
       - uses: JuliaRegistries/TagBot@v1
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
+          ssh: ${{ secrets.DOCUMENTER_KEY }} # see https://juliadocs.github.io/Documenter.jl/stable/man/hosting/#GitHub-Actions

.github/workflows/Tier1.yml

@@ -1,4 +1,4 @@
-name: Tier1
+name: Tier 1
 on:
   push:
     branches:
@@ -12,9 +12,6 @@ on:
     paths-ignore:
       - 'LICENSE.md'
       - 'README.md'
-  release:
-    types:
-      - created
 jobs:
   ci:
     runs-on: ${{ matrix.os }}
@@ -33,11 +30,4 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       - uses: julia-actions/julia-uploadcodecov@v0.1
-        if: startsWith(matrix.os, 'Ubuntu')
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-      - uses: julia-actions/julia-docdeploy@releases/v1
-        if: ${{ startsWith(matrix.os, 'Ubuntu') && startsWith(matrix.julia-version, '1.5') }}
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
+        if:  ${{ startsWith(matrix.os, 'Ubuntu') && startsWith(matrix.julia-version, '1.5') }}

.github/workflows/documenter.yml

@@ -0,0 +1,19 @@
+name: Documenter
+on:
+  push:
+    branches: [master]
+    tags: [v*]
+  pull_request:
+    branches:
+      - master
+jobs:
+  Documenter:
+    name: Documentation
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-docdeploy@latest
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}

Artifacts.toml

@@ -5,21 +5,21 @@
 [TestData]
 # compute this using
 # using Tar, Inflate, SHA
-# filename = "download?version=2" # I just used wget for the URL below and this is how it saved it
+# filename = "download?version=4" # I just used wget for the URL below and this is how it saved it
 # println("sha256: ", bytes2hex(open(sha256, filename)))
 # println("git-tree-sha1: ", Tar.tree_hash(IOBuffer(inflate_gzip(filename))))
 # from https://julialang.github.io/Pkg.jl/dev/artifacts/
-git-tree-sha1 = "a3955a5f747d01e628944b1031b44e31f027ebef"
+git-tree-sha1 = "75260d131b693f26e5834adf855f4c35d627348d"
 lazy = true
 
     [[TestData.download]]
-    # this is the SHA from https://osf.io/djaqb/download?version=2
-    sha256 = "b6273f0cfeb5b12e2afede33de6d68a8d926e7b684cf071c7622f1e6ef7aa64a"
+    # this is the SHA from https://osf.io/djaqb/download?version=4
+    sha256 = "d179cadfb27fc638fd8a06b0d58f5f2916f786c5719ab22de93fedb6129726f4"
     # when updating this, make sure to change to change the version number,
     # because if the version number isn't included, it will always point to the
     # latest version, which means it will break existing users when we update
     # between releases.
-    url = "https://osf.io/djaqb/download?version=2"
+    url = "https://osf.io/djaqb/download?version=4"
 
     # for future work on using xz-compressed data:
     # Julia invokes wget without using HTTP metadata, so we need the link

NEWS.md

@@ -1,3 +1,15 @@
+MixedModels v3.1 Release Notes
+========================
+
+* `simulate!` and thus `parametricbootstrap` methods for `GeneralizedLinearMixedModel` [#418].
+* Documented inconsistent behavior in `sdest` and `varest` `GeneralizedLinearMixedModel` [#418].
+
+MixedModels v3.0.2 Release Notes
+========================
+
+* Compatibility updates
+* Minor updates for formatting in various `show` method for `VarCorr`.
+
 MixedModels v3.0 Release Notes
 ========================
 
@@ -43,12 +55,12 @@ Principal components
 ----------------------------------
 
 * An `AbstractReMat` type has now been introduced to support [#380] work on constrained
-  random-effects structures and random-effects structures appropriate for applications 
-  in GLM-based decovolution as used in fMRI and EEG (see e.g. [unfold.jl](https://github.com/unfoldtoolbox/unfold.jl).) 
+  random-effects structures and random-effects structures appropriate for applications
+  in GLM-based decovolution as used in fMRI and EEG (see e.g. [unfold.jl](https://github.com/unfoldtoolbox/unfold.jl).)
 * Similarly, a constructor for `FeMat{::SparseMatrixCSC,S}` has been introduced [#309].
-  Currently, this constructor assumes a full-rank matrix, but the work on rank 
+  Currently, this constructor assumes a full-rank matrix, but the work on rank
   deficiency may be extended to this constructor as well.
-* Analogous to `AbstractReMat`, an `AbstractReTerm <: AbstractTerm` type  has been introduced [#395]. 
+* Analogous to `AbstractReMat`, an `AbstractReTerm <: AbstractTerm` type  has been introduced [#395].
   Terms created with `zerocorr` are of type `ZeroCorr <: AbstractReTerm`.
 
 Availability of test data sets
@@ -85,3 +97,4 @@ Package dependencies
 [#384]: https://github.com/JuliaStats/MixedModels.jl/issues/384
 [#390]: https://github.com/JuliaStats/MixedModels.jl/issues/390
 [#395]: https://github.com/JuliaStats/MixedModels.jl/issues/395
+[#418]: https://github.com/JuliaStats/MixedModels.jl/issues/418

Project.toml

@@ -1,7 +1,7 @@
 name = "MixedModels"
 uuid = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
 author = ["Phillip Alday <me@phillipalday.com>", "Douglas Bates <dmbates@gmail.com>", "Jose Bayoan Santiago Calderon <jbs3hp@virginia.edu>"]
-version = "3.0.0-DEV"
+version = "3.1.0"
 
 [deps]
 Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
@@ -24,10 +24,10 @@ StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
-Arrow = "0.3"
+Arrow = "0.3, 0.4"
 BlockArrays = "0.11, 0.12"
 DataAPI = "1.1, 1.2, 1.3"
-Distributions = "0.21, 0.22, 0.23"
+Distributions = "0.21, 0.22, 0.23, 0.24"
 GLM = "1"
 NLopt = "0.5, 0.6"
 PooledArrays = "0.5"

README.md

@@ -49,52 +49,104 @@ Typical distribution forms are _Bernoulli_ for binary data or _Poisson_ for coun
 
 |OS|OS Version|Arch|Julia|Tier|
 |:-:|:-:|:-:|:-:|:-:|
-|Linux|Ubuntu 18.04|x64|v1.4|1|
-|macOS|Catalina 10.15|x64|v1.4|1|
-|Windows|Server 2019|x64|v1.4|1|
-|Linux|Ubuntu 18.04|x86|v1.4|2|
-|Windows|Server 2019|x86|v1.4|2|
-
-## Version 2.0.0
-
-Version 2.0.0 contains some user-visible changes and many changes in the underlying code.
-
-The user-visible changes include:
-
-- Update formula specification to `StatsModels v"0.6.2"`, allowing for function calls within the fixed-effects terms and for interaction terms on the left-hand side of a random-effects term.
-
-- Use of properties in a model in addition to extractor functions.  For example, to obtain the covariance parameter, $\theta$, from a model, the recommended approach now is to access the `θ` property, as in `m.θ`, instead of the extractor `getθ(m)`.
-
-- `bootstrap` is now named `parametricbootstrap` to avoid conflict with a similar name in the `Bootstrap` package.  The bootstrap sample is returned as a `Table`.
-
-- A `fit` method for the abstract type `MixedModel` has been added.  It is called as
-
-```
-julia> using Tables, MixedModels
-
-julia> Dyestuff = columntable((batch = string.(repeat('A':'F', inner=5)),
-       yield = [1545, 1440, 1440, 1520, 1580, 1540, 1555, 1490, 1560, 1495, 1595, 1550, 1605,
-        1510, 1560, 1445, 1440, 1595, 1465, 1545, 1595, 1630, 1515, 1635, 1625, 1520, 1455,
-        1450, 1480, 1445]));
-
-julia> m1 = fit(MixedModel, @formula(yield ~ 1 + (1|batch)), Dyestuff)
-    Linear mixed model fit by maximum likelihood
-     yield ~ 1 + (1 | batch)
-       logLik   -2 logLik     AIC        BIC    
-     -163.66353  327.32706  333.32706  337.53065
-
-        Variance components:
-                  Column    Variance  Std.Dev.
-     batch    (Intercept)  1388.3334 37.260347
-     Residual              2451.2500 49.510100
-     Number of obs: 30; levels of grouping factors: 6
-
-     Fixed-effects parameters:
-    ──────────────────────────────────────────────────
-                 Estimate  Std.Error  z value  P(>|z|)
-    ──────────────────────────────────────────────────
-    (Intercept)    1527.5    17.6946   86.326   <1e-99
-    ──────────────────────────────────────────────────
+|Linux|Ubuntu 18.04|x64|v1.4, 1.5|1|
+|macOS|Catalina 10.15|x64|v1.4, 1.5|1|
+|Windows|Server 2019|x64|v1.4, 1.5 |1|
+
+Upon release of the next Julia LTS, Tier 1 will become Tier 2 and Julia LTS will become Tier 1.
+
+## Version 3.0.0
+
+Version 3.0.0 contains some user-visible changes and many changes in the underlying code.
+
+Please see [NEWS](NEWS.md) for a complete overview.
+The most dramatic user-facing change is a re-working of `parametricbootstrap` for speed and convenience.
+Additionally, several formula features have been added and [the handling of rank deficiency has changed](https://juliastats.org/MixedModels.jl/dev/rankdeficiency/).
+
+## Quick Start
+```julia-repl
+julia> using MixedModels
+
+julia> m1 = fit(MixedModel, @formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff))
+Linear mixed model fit by maximum likelihood
+ yield ~ 1 + (1 | batch)
+   logLik   -2 logLik     AIC        BIC
+  -163.6635   327.3271   333.3271   337.5307
+
+Variance components:
+            Column   VarianceStd.Dev.
+batch    (Intercept)  1388.33 37.2603
+Residual              2451.25 49.5101
+ Number of obs: 30; levels of grouping factors: 6
+
+  Fixed-effects parameters:
+────────────────────────────────────────────────
+              Coef.  Std. Error      z  Pr(>|z|)
+────────────────────────────────────────────────
+(Intercept)  1527.5     17.6946  86.33    <1e-99
+────────────────────────────────────────────────
+
+
+julia> using Random
+
+julia> bs = parametricbootstrap(MersenneTwister(42), 1000, m1);
+Progress: 100%%|████████████████████████████████████████████████| Time: 0:00:00
+
+julia> propertynames(bs)
+13-element Vector{Symbol}:
+ :allpars
+ :objective
+ :σ
+ :β
+ :se
+ :coefpvalues
+ :θ
+ :σs
+ :λ
+ :inds
+ :lowerbd
+ :bstr
+ :fcnames
+
+julia> bs.coefpvalues # returns a row table
+1000-element Vector{NamedTuple{(:iter, :coefname, :β, :se, :z, :p), Tuple{Int64, Symbol, Float64, Float64, Float64, Float64}}}:
+ (iter = 1, coefname = Symbol("(Intercept)"), β = 1517.0670832927115, se = 20.76271142094811, z = 73.0669059804057, p = 0.0)
+ (iter = 2, coefname = Symbol("(Intercept)"), β = 1503.5781855888436, se = 8.1387737362628, z = 184.7425956676446, p = 0.0)
+ (iter = 3, coefname = Symbol("(Intercept)"), β = 1529.2236379016574, se = 16.523824785737837, z = 92.54659001356465, p = 0.0)
+ ⋮
+ (iter = 998, coefname = Symbol("(Intercept)"), β = 1498.3795009457242, se = 25.649682012258104, z = 58.417079019913054, p = 0.0)
+ (iter = 999, coefname = Symbol("(Intercept)"), β = 1526.1076747922416, se = 16.22412120273579, z = 94.06411945042063, p = 0.0)
+ (iter = 1000, coefname = Symbol("(Intercept)"), β = 1557.7546433870125, se = 12.557577103806015, z = 124.04898098653763, p = 0.0)
+
+julia> using DataFrames
+
+julia> DataFrame(bs.coefpvalues) # puts it into a DataFrame
+1000×6 DataFrame
+│ Row  │ iter  │ coefname    │ β       │ se      │ z       │ p       │
+│      │ Int64 │ Symbol      │ Float64 │ Float64 │ Float64 │ Float64 │
+├──────┼───────┼─────────────┼─────────┼─────────┼─────────┼─────────┤
+│ 1    │ 1     │ (Intercept) │ 1517.07 │ 20.7627 │ 73.0669 │ 0.0     │
+│ 2    │ 2     │ (Intercept) │ 1503.58 │ 8.13877 │ 184.743 │ 0.0     │
+│ 3    │ 3     │ (Intercept) │ 1529.22 │ 16.5238 │ 92.5466 │ 0.0     │
+⋮
+│ 998  │ 998   │ (Intercept) │ 1498.38 │ 25.6497 │ 58.4171 │ 0.0     │
+│ 999  │ 999   │ (Intercept) │ 1526.11 │ 16.2241 │ 94.0641 │ 0.0     │
+│ 1000 │ 1000  │ (Intercept) │ 1557.75 │ 12.5576 │ 124.049 │ 0.0     │
+
+julia> DataFrame(bs.β)
+1000×3 DataFrame
+│ Row  │ iter  │ coefname    │ β       │
+│      │ Int64 │ Symbol      │ Float64 │
+├──────┼───────┼─────────────┼─────────┤
+│ 1    │ 1     │ (Intercept) │ 1517.07 │
+│ 2    │ 2     │ (Intercept) │ 1503.58 │
+│ 3    │ 3     │ (Intercept) │ 1529.22 │
+⋮
+│ 998  │ 998   │ (Intercept) │ 1498.38 │
+│ 999  │ 999   │ (Intercept) │ 1526.11 │
+│ 1000 │ 1000  │ (Intercept) │ 1557.75 │
 ```
 
+## Funding Acknowledgement
+
 The development of this package was supported by the Center for Interdisciplinary Research, Bielefeld (ZiF)/Cooperation Group "Statistical models for psychological and linguistic data".

benchmark/Project.toml

@@ -0,0 +1,4 @@
+[deps]
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+MixedModels = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
+PkgBenchmark = "32113eaa-f34f-5b0d-bd6c-c81e245fc73d"

docs/make.jl

@@ -5,6 +5,7 @@ using MixedModels
 makedocs(
     root = joinpath(dirname(pathof(MixedModels)), "..", "docs"),
     sitename = "MixedModels",
+    doctest = true,
     pages = [
         "index.md",
         "constructors.md",