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Using VitePress for documentation #808

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8c19a11
added vitepress
ajinkya-k Mar 6, 2025
6691c35
using documenter header id style
ajinkya-k Mar 10, 2025
2a9cb31
more descriptive id for internal structure header
ajinkya-k Mar 10, 2025
3f6694c
changed urls to juliatstats versions
ajinkya-k Mar 25, 2025
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1 change: 1 addition & 0 deletions docs/Project.toml
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Expand Up @@ -4,6 +4,7 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
DocumenterVitepress = "4710194d-e776-4893-9690-8d956a29c365"
FreqTables = "da1fdf0e-e0ff-5433-a45f-9bb5ff651cb1"
Gadfly = "c91e804a-d5a3-530f-b6f0-dfbca275c004"
MixedModels = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
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26 changes: 17 additions & 9 deletions docs/make.jl
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Expand Up @@ -2,23 +2,31 @@ using Documenter
using MixedModels
using StatsAPI
using StatsBase
using DocumenterVitepress

makedocs(;
sitename="MixedModels",
doctest=true,
pages=[
"index.md",
"constructors.md",
"optimization.md",
"GaussHermite.md",
"prediction.md",
"bootstrap.md",
"rankdeficiency.md",
"mime.md",
"api.md",
"Articles" => [
"constructors.md",
"optimization.md",
"GaussHermite.md",
"prediction.md",
"bootstrap.md",
"rankdeficiency.md",
"mime.md"
],
"api.md"
],
format = DocumenterVitepress.MarkdownVitepress(
repo = "https://github.com/JuliaStats/MixedModels.jl",
),
clean = true
)

deploydocs(;
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This needs to be DocumenterVitepress.deploydocs in DV v0.2

repo="github.com/JuliaStats/MixedModels.jl.git", push_preview=true, devbranch="main"
target = "build",
repo="github.com/JuliaStats/MixedModels.jl.git", push_preview=true, devbranch="ahk/doc-vite"
)
2 changes: 1 addition & 1 deletion docs/src/optimization.md
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Expand Up @@ -92,7 +92,7 @@ are sometimes called the *best linear unbiased predictors* or BLUPs of the rando
Although BLUPs an appealing acronym, I don’t find the term particularly instructive (what is a “linear unbiased predictor” and in what sense are these the “best”?) and prefer the term “conditional modes”, because these are the values of $\bf b$ that maximize the density of the conditional distribution $\mathcal{B} | \mathcal{Y} = {\bf y}$.
For a linear mixed model, where all the conditional and unconditional distributions are Gaussian, these values are also the *conditional means*.

## Internal structure of $\Lambda_\theta$ and $\bf Z$
## [Internal structure of $\Lambda_\theta$ and $\bf Z$](@id Internal-structure-of-random-effects-coefficients-and-model-matrix)

In the types of `LinearMixedModel` available through the `MixedModels` package, groups of random effects and the corresponding columns of the model matrix, $\bf Z$, are associated with *random-effects terms* in the model formula.

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