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RePsychLing · Jun 27, 2024 · 6582f75 · 6582f75
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diff --git a/AoGPlots.qmd b/AoGPlots.qmd
@@ -4,7 +4,7 @@ title: "Creating multi-panel plots"
 
 This notebook shows creating a multi-panel plot similar to Figure 2 of @Fuehner2021.
 
-The data are available from the SMLP2023 example datasets.
+The data are available from the SMLP2024 example datasets.
 
 ```{julia}
 #| code-fold: true
@@ -14,7 +14,7 @@ using CairoMakie   # for displaying static plots
 using DataFrames
 using Statistics
 using StatsBase
-using SMLP2023: dataset
+using SMLP2024: dataset
 
 CairoMakie.activate!(; type="svg") # use SVG (other options include PNG)
 ```

diff --git a/Project.toml b/Project.toml
@@ -1,6 +1,6 @@
-name = "SMLP2023"
-uuid = "dc5ec7c6-28fd-4523-b102-f28df6bb352f"
-authors = ["Phillip Alday <[email protected]>", "Douglas Bates <[email protected]>", "Reinhold Kliegl <[email protected]>", "Julius Krumbiegel <[email protected]>"]
+name = "SMLP2024"
+uuid = "3c8ca5f6-7797-4ff8-8247-191023d8f95a"
+authors = ["Phillip Alday <[email protected]>", "Douglas Bates <[email protected]>", "Reinhold Kliegl <[email protected]>"]
 version = "0.1.0"
 
 [deps]

diff --git a/README.md b/README.md
@@ -1,13 +1,13 @@
-# SMLP2023
+# SMLP2024
 
-SMLP2023: Advanced methods in frequentist statistics with Julia
+SMLP2024: Advanced methods in frequentist statistics with Julia
 
-The rendered website version of the course materials is available [here](https://repsychling.github.io/SMLP2023/).
+The rendered website version of the course materials is available [here](https://repsychling.github.io/SMLP2024/).
 
 This repository uses [Quarto](https://quarto.org). To be able to render all the pages, you will need an appropriate Jupyter kernel installed and the local environment instantiated.
 
 ```sh
-~/SMLP2023$ julia
+~/SMLP2024$ julia
 
 julia> using Pkg
 
@@ -23,14 +23,14 @@ julia> installkernel("julia", "--threads=auto", "--project=@.")
 "~/.local/share/jupyter/kernels/julia-1.9"
 
 julia> Pkg.activate(".")
-  Activating project at `~/SMLP2023`
+  Activating project at `~/SMLP2024`
 
 julia> Pkg.instantiate()
 < lots of output >
 
 julia> exit()
 
-~/SMLP2023$ quarto preview
+~/SMLP2024$ quarto preview
 
 < lots of output >
 

diff --git a/_quarto.yml b/_quarto.yml
@@ -1,6 +1,6 @@
 project:
   type: website
-  title: "SMLP2023"
+  title: "SMLP2024"
   execute-dir: project
   output-dir: _build/
   render:
@@ -56,9 +56,9 @@ format:
     fig-height: 6
 
 website:
-  title: "SMLP2023: Advanced Frequentist Track"
-  site-url: https://RePsychLing.github.io/SMLP2023
-  repo-url: https://github.com/RePsychLing/SMLP2023
+  title: "SMLP2024: Advanced Frequentist Track"
+  site-url: https://RePsychLing.github.io/SMLP2024
+  repo-url: https://github.com/RePsychLing/SMLP2024
   repo-actions: [edit, issue]
   # reader-mode: true
   navbar:
@@ -69,7 +69,7 @@ website:
     #   - about.qmd
     right:
       - icon: github
-        href: https://github.com/RePsychLing/SMLP2023
+        href: https://github.com/RePsychLing/SMLP2024
   page-navigation: true
   back-to-top-navigation: true
   sidebar:

diff --git a/arrow.qmd b/arrow.qmd
@@ -11,7 +11,7 @@ The columns can be of different types including numeric values, character string
 
 An Arrow file can be read or written from R, Python, Julia and many other languages.  Somewhat confusingly in R and Python the name `feather`, which refers to an earlier version of the storage format, is used in some function names like `read_feather`.
 
-Internally, the SMLP2023 package uses Arrow to store all of its datasets.
+Internally, the SMLP2024 package uses Arrow to store all of its datasets.
 
 # The Emotikon data
 

diff --git a/bootstrap.qmd b/bootstrap.qmd
@@ -36,7 +36,7 @@ using DataFrames
 using MixedModels
 using MixedModelsMakie
 using Random
-using SMLP2023: dataset
+using SMLP2024: dataset
 
 using AlgebraOfGraphics: AlgebraOfGraphics as AoG
 CairoMakie.activate!(; type="svg") # use SVG (other options include PNG)

diff --git a/contrasts_fggk21.qmd b/contrasts_fggk21.qmd
@@ -45,7 +45,7 @@ using DataFrames
 using DataFrameMacros
 using MixedModels
 using ProgressMeter
-using SMLP2023: dataset
+using SMLP2024: dataset
 using Statistics
 using StatsBase
 

diff --git a/contrasts_kwdyz11.qmd b/contrasts_kwdyz11.qmd
@@ -8,7 +8,7 @@ jupyter: julia-1.9
 using Chain
 using DataFrames
 using MixedModels
-using SMLP2023: dataset
+using SMLP2024: dataset
 using StatsBase
 using StatsModels
 

diff --git a/fggk21.qmd b/fggk21.qmd
@@ -33,7 +33,7 @@ using ProgressMeter
 using Random
 using Statistics
 using StatsBase
-using SMLP2023: dataset
+using SMLP2024: dataset
 
 ProgressMeter.ijulia_behavior(:clear)
 CairoMakie.activate!(; type="svg")

diff --git a/glmm.qmd b/glmm.qmd
@@ -14,7 +14,7 @@ using DataFrameMacros
 using DataFrames
 using MixedModels
 using MixedModelsMakie
-using SMLP2023: dataset
+using SMLP2024: dataset
 
 CairoMakie.activate!(; type="svg")
 

diff --git a/index.qmd b/index.qmd
@@ -4,7 +4,7 @@ subtitle: "Advanced methods in frequentist statistics with Julia"
 author: "Phillip Alday, Reinhold Kliegl and Douglas Bates"
 ---
 
-This site provides materials for the **Advanced frequentist methods** stream of the [Summer School on Statistical Methods](https://vasishth.github.io/smlp2023/) to be held at the University of Potsdam, 11-15 September, 2023.
+This site provides materials for the **Advanced frequentist methods** stream of the [Summer School on Statistical Methods](https://vasishth.github.io/smlp2024/) to be held at the University of Potsdam, 11-15 September, 2024.
 
 # Installation of tools prior to the course
 
@@ -16,11 +16,11 @@ Because most students in the course will have more experience with [R](https://r
 We will assume that you have git installed and are able to clone a repository from github.
 If not, [Happy Git with R](https://happygitwithr.com/) is a good place to learn about git for data science.
 
-This website is built using [quarto](https://quarto.org), described below, from the [repository](https://github.com/RePsychLing/SMLP2023).
+This website is built using [quarto](https://quarto.org), described below, from the [repository](https://github.com/RePsychLing/SMLP2024).
 Clone this repository with, e.g.
 
 ```sh
-git clone https://github.com/RePsychLing/SMLP2023
+git clone https://github.com/RePsychLing/SMLP2024
 ```
 
 ## Julia Programming Language

diff --git a/kb07.qmd b/kb07.qmd
@@ -14,7 +14,7 @@ using DataFrames
 using MixedModels
 using ProgressMeter
 using Random
-using SMLP2023: dataset
+using SMLP2024: dataset
 
 CairoMakie.activate!(; type="svg")
 

diff --git a/kkl15.qmd b/kkl15.qmd
@@ -15,9 +15,9 @@ This comparison is of interest because a few years after the publication of @Kli
 
 Here we also include two additional experimental manipulations of target size and orientation of cue rectangle. A similar analysis was reported in the parsimonious mixed-model paper [@Bates2015]; it was also used in a paper of GAMMs [@Baayen2017]. Data and R scripts of those analyses are also available in [R-package RePsychLing](https://github.com/dmbates/RePsychLing/tree/master/data/).
 
-The analysis is based on log-transformed reaction times `lrt`, indicated by a _boxcox()_ check of model residuals. 
+The analysis is based on log-transformed reaction times `lrt`, indicated by a _boxcox()_ check of model residuals.
 
-In this vignette we focus on the reduction of model complexity. And we start with a quote: 
+In this vignette we focus on the reduction of model complexity. And we start with a quote:
 
 “Neither the [maximal] nor the [minimal] linear mixed models are appropriate for most repeated measures analysis. Using the [maximal] model is generally wasteful and costly in terms of statiscal power for testing hypotheses. On the other hand, the [minimal] model fails to account for nontrivial correlation among repeated measurements. This results in inflated [T]ype I error rates when non-negligible correlation does in fact exist. We can usually find middle ground, a covariance model that adequately accounts for correlation but is more parsimonious than the [maximal] model. Doing so allows us full control over [T]ype I error rates without needlessly sacrificing power.”
 
@@ -41,7 +41,7 @@ using MixedModels
 using MixedModelsMakie
 using ProgressMeter
 using Random
-using SMLP2023: dataset
+using SMLP2024: dataset
 using StatsBase
 
 ProgressMeter.ijulia_behavior(:clear)
@@ -135,15 +135,15 @@ contrasts = Dict(
 
 ```{julia}
 m_max_rt = let
-  form = @formula rt ~ 1 + CTR * size * cardinal + 
+  form = @formula rt ~ 1 + CTR * size * cardinal +
                            (1 + CTR * size * cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
 ```
 
 ```{julia}
 m_cpx_rt = let
-  form = @formula rt ~ 1 + CTR * size * cardinal + 
+  form = @formula rt ~ 1 + CTR * size * cardinal +
                            (1 + CTR + size + cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
@@ -174,7 +174,7 @@ This is the maximum LMM for the design.
 
 ```{julia}
 m_max = let
-  form = @formula log(rt) ~ 1 + CTR * size * cardinal + 
+  form = @formula log(rt) ~ 1 + CTR * size * cardinal +
                            (1 + CTR * size * cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
@@ -201,7 +201,7 @@ Force CPs to zero.
 
 ```{julia}
 m_zcp1 = let
-  form = @formula log(rt) ~ 1 + CTR * size * cardinal + 
+  form = @formula log(rt) ~ 1 + CTR * size * cardinal +
                    zerocorr(1 + CTR * size * cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
@@ -225,7 +225,7 @@ Take out VC for interactions.
 
 ```{julia}
 m_zcp1_rdc = let
-  form = @formula log(rt) ~ 1 + CTR * size * cardinal + 
+  form = @formula log(rt) ~ 1 + CTR * size * cardinal +
                    zerocorr(1 + CTR + size + cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
@@ -252,7 +252,7 @@ gof_summary = let
   mods = eval.(nms)
   lrt = MixedModels.likelihoodratiotest(m_zcp1_rdc, m_zcp1, m_max)
   DataFrame(;
-    name = nms, 
+    name = nms,
     dof=dof.(mods),
     deviance=round.(deviance.(mods), digits=0),
     AIC=round.(aic.(mods),digits=0),
@@ -271,7 +271,7 @@ Extend zcp-reduced LMM with CPs
 
 ```{julia}
 m_prm1 = let
-  form = @formula log(rt) ~ 1 + CTR * size * cardinal + 
+  form = @formula log(rt) ~ 1 + CTR * size * cardinal +
                            (1 + CTR + size + cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
@@ -299,7 +299,7 @@ gof_summary = let
   mods = eval.(nms)
   lrt = MixedModels.likelihoodratiotest(m_prm1, m_zcp1, m_max)
   DataFrame(;
-    name = nms, 
+    name = nms,
     dof=dof.(mods),
     deviance=round.(deviance.(mods), digits=0),
     AIC=round.(aic.(mods),digits=0),
@@ -319,7 +319,7 @@ Take out interaction VCs.
 
 ```{julia}
 m_cpx = let
-  form = @formula log(rt) ~ 1 + CTR * size * cardinal + 
+  form = @formula log(rt) ~ 1 + CTR * size * cardinal +
                            (1 + CTR + size + cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
@@ -330,7 +330,7 @@ Take out interaction VCs.
 
 ```{julia}
 m_zcp2 = let
-  form = @formula log(rt) ~ 1 + CTR * size * cardinal + 
+  form = @formula log(rt) ~ 1 + CTR * size * cardinal +
                    zerocorr(1 + CTR + size + cardinal | Subj)
   fit(MixedModel, form, dat; contrasts)
 end
@@ -344,7 +344,7 @@ gof_summary = let
   mods = eval.(nms)
   lrt = MixedModels.likelihoodratiotest(m_zcp2, m_cpx, m_max)
   DataFrame(;
-    name = nms, 
+    name = nms,
     dof=dof.(mods),
     deviance=round.(deviance.(mods), digits=0),
     AIC=round.(aic.(mods),digits=0),
@@ -553,7 +553,7 @@ draw(
   data(tbl) *
   mapping(
     [:β02, :β03, :β04] .=> "Experimental effect size [ms]";
-    color=dims(1) => 
+    color=dims(1) =>
     renamer(["spatial effect", "object effect", "attraction effect"]) =>
     "Experimental effects",
   ) *

diff --git a/kwdyz11.qmd b/kwdyz11.qmd
@@ -40,7 +40,7 @@ using Distributions
 using MixedModels
 using MixedModelsMakie
 using Random
-using SMLP2023: dataset
+using SMLP2024: dataset
 using StatsBase
 
 CairoMakie.activate!(; type="svg")
@@ -351,7 +351,7 @@ draw(
   data(tbl) *
   mapping(
     [:σ1, :σ2, :σ3, :σ4] .=> "Standard deviations [ms]";
-    color=dims(1) => 
+    color=dims(1) =>
     renamer(append!(["Grand mean"],["spatial", "object", "attraction"] .* " effect")) =>
     "Variance components",
   ) *

diff --git a/largescaledesigned.qmd b/largescaledesigned.qmd
@@ -18,7 +18,7 @@ using DataFrames
 using Effects
 using MixedModels
 using MixedModelsMakie
-using SMLP2023: dataset
+using SMLP2024: dataset
 using StandardizedPredictors
 using StatsBase
 

diff --git a/mrk17.qmd b/mrk17.qmd
@@ -12,7 +12,7 @@ using CategoricalArrays
 using DataFrames
 using MixedModels
 using MixedModelsMakie
-using SMLP2023: dataset
+using SMLP2024: dataset
 using Statistics: mean, std
 ```
 
@@ -118,7 +118,7 @@ using CategoricalArrays
 using DataFrames
 using MixedModels
 using MixedModelsMakie
-using SMLP2023: dataset
+using SMLP2024: dataset
 using Statistics: mean, std
 
 ```

diff --git a/pkg.qmd b/pkg.qmd
@@ -8,7 +8,7 @@ Julia packages can be configured (in a file called `Project.toml`) on a per-proj
 The packaged sources and compiled versions are stored in a central location, e.g. `~/.julia/packages` and `~/.julia/compiled` on Linux systems, but the configuration of packages to be used can be local to a project.
 The `Pkg` package is used to modify the local project's configuration.
 (An alternative is "package mode" in the read-eval-print-loop or REPL, which we will show at the summer school.)
-Start julia in the directory of the cloned `SMLP2023` repository
+Start julia in the directory of the cloned `SMLP2024` repository
 ```{julia}
 #| eval: false
 using Pkg        # there's a package called 'Pkg' to manipulate package configs

diff --git a/profiling.qmd b/profiling.qmd
@@ -31,7 +31,7 @@ using CairoMakie
 using MixedModels
 using MixedModelsMakie
 using Random
-using SMLP2023: dataset
+using SMLP2024: dataset
 
 CairoMakie.activate!(; type="svg")
 import ProgressMeter

diff --git a/sleepstudy.qmd b/sleepstudy.qmd
@@ -30,14 +30,14 @@ using MixedModelsMakie # diagnostic plots
 using ProgressMeter
 using Random           # random number generators
 using RCall            # call R from Julia
-using SMLP2023
-using SMLP2023: dataset
+using SMLP2024
+using SMLP2024: dataset
 
 ProgressMeter.ijulia_behavior(:clear)
 CairoMakie.activate!(; type="svg")
 ```
 
-The `sleepstudy` data are one of the datasets available with the `MixedModels` package.  It is re-exported by the `SMLP2023` package's `dataset` function.
+The `sleepstudy` data are one of the datasets available with the `MixedModels` package.  It is re-exported by the `SMLP2024` package's `dataset` function.
 
 ```{julia}
 sleepstudy = dataset("sleepstudy")