Fix sticker

.gitignore

@@ -10,3 +10,4 @@ src/*.dll
 /Meta/
 src/Makevars
 src/Makevars.win
+docs/*

DESCRIPTION

@@ -19,7 +19,7 @@ Maintainer: Maarten Marsman <m.marsman@uva.nl>
 Description: Bayesian variable selection methods for analyzing the structure of a Markov random field model for a network of binary and/or ordinal variables. 
 Copyright: Includes datasets 'ADHD' and 'Boredom', which are licensed under CC-BY 4. See individual data documentation for license and citation.
 License: GPL (>= 2)
-URL: https://Bayesian-Graphical-Modelling-Lab.github.io/bgms/
+URL: https://Bayesian-Graphical-Modelling-Lab.github.io/bgms/, https://github.com/Bayesian-Graphical-Modelling-Lab/bgms
 BugReports: https://github.com/Bayesian-Graphical-Modelling-Lab/bgms/issues
 Imports: 
     Rcpp (>= 1.0.7), 

README.md

@@ -1,4 +1,3 @@
-
 <!-- badges: start -->
 
 [![CRAN
@@ -7,7 +6,12 @@ Version](https://www.r-pkg.org/badges/version/bgms)](https://cran.r-project.org/
 [![Total](https://cranlogs.r-pkg.org/badges/grand-total/bgms)](https://cran.r-project.org/package=bgms)
 <!-- badges: end -->
 
-# bgms <a href="https://bayesiangraphicalmodeling.com"><img src="inst/bgms_sticker.svg" height="200" align="right" /></a>
+<div style="display:flex; align-items:center; justify-content:space-between;">
+  <h1>bgms</h1>
+  <a href="https://bayesiangraphicalmodeling.com">
+    <img src="man/figures/bgms_sticker.svg" height="200" alt="bgms website" />
+  </a>
+</div>
 
 **Bayesian analysis of graphical models with binary and ordinal
 variables**
@@ -23,28 +27,28 @@ corresponding pseudoposterior distribution of the model parameters.
 
 The package has two main entry points:
 
-- `bgm()` – estimates a single network in a one-sample design.  
+- `bgm()` – estimates a single network in a one-sample design.
 - `bgmCompare()` – compares networks between groups in an
   independent-sample design.
 
 ## Effect selection
 
 Both functions support **effect selection** with spike-and-slab priors:
 
-- **Edges in one-sample designs**:  
+- **Edges in one-sample designs**:
   `bgm()` models the presence or absence of edges between variables.
   Posterior inclusion probabilities indicate the plausibility of each
   edge and can be converted into Bayes factors for conditional
   independence tests (see Marsman et al., 2025; Sekulovski et al.,
   2024).
 
-- **Communities/clusters in one-sample designs**:  
+- **Communities/clusters in one-sample designs**:
   `bgm()` can also model community structure. Posterior probabilities
   for the number of clusters quantify the plausibility of clustering
   solutions and can be converted into Bayes factors (see Sekulovski et
   al., 2025).
 
-- **Group differences in independent-sample designs**:  
+- **Group differences in independent-sample designs**:
   `bgmCompare()` models differences in edge weights and category
   thresholds between groups. Posterior inclusion probabilities indicate
   the plausibility of parameter differences and can be converted into
@@ -56,9 +60,9 @@ Both functions support **effect selection** with spike-and-slab priors:
 For worked examples and tutorials, see the package vignettes:
 
 - [Getting
-  Started](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/intro.html)  
+  Started](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/intro.html)
 - [Model
-  Comparison](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/comparison.html)  
+  Comparison](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/comparison.html)
 - [Diagnostics and Spike-and-Slab
   Summaries](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/diagnostics.html)
 
@@ -86,7 +90,7 @@ association between the unconnected pair.
 When analyzing an MRF, we often want to compare competing hypotheses:
 
 - **Edge presence vs. edge absence** (conditional dependence
-  vs. independence) in one-sample designs.  
+  vs. independence) in one-sample designs.
 - **Parameter difference vs. parameter equivalence** in
   independent-sample designs.
 
@@ -99,7 +103,7 @@ Bayesian inference avoids this problem. Using **inclusion Bayes
 factors** (Huth et al., 2023; Sekulovski et al., 2024), we can quantify
 evidence in both directions:
 
-- **Evidence of edge presence** vs. **evidence of edge absence**, or  
+- **Evidence of edge presence** vs. **evidence of edge absence**, or
 - **Evidence of parameter difference** vs. **evidence of parameter
   equivalence**.
 
@@ -112,9 +116,9 @@ evidence*.
 The current developmental version can be installed with
 
 ``` r
-if (!requireNamespace("remotes")) { 
-  install.packages("remotes")   
-}   
+if (!requireNamespace("remotes")) {
+  install.packages("remotes")
+}
 remotes::install_github("Bayesian-Graphical-Modelling-Lab/bgms")
 ```
 

Readme.Rmd

@@ -3,27 +3,33 @@ output: github_document
 bibliography: inst/REFERENCES.bib
 csl: inst/apa.csl
 ---
-  
+
 ```{r, echo = FALSE, message=F}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
   fig.path = "man/figures/README-",
   dev = "png",
   dpi = 200,
-  fig.align = "center",
   knitr::opts_chunk$set(comment = NA)
 )
 library(bgms)
 ```
 
+<!-- # bgms <a href="https://bayesiangraphicalmodeling.com"><img src="man/figures/bgms_sticker.svg" height="200" align="right" alt="bgms website" /></a> -->
+<div style="display:flex; align-items:center; justify-content:space-between;">
+  <h1>bgms</h1>
+  <a href="https://bayesiangraphicalmodeling.com">
+    <img src="man/figures/bgms_sticker.svg" height="200" alt="bgms website" />
+  </a>
+</div>
+
 <!-- badges: start -->
 [![CRAN Version](https://www.r-pkg.org/badges/version/bgms)](https://cran.r-project.org/package=bgms)
 [![Downloads](https://cranlogs.r-pkg.org/badges/bgms)](https://cran.r-project.org/package=bgms)
 [![Total](https://cranlogs.r-pkg.org/badges/grand-total/bgms)](https://cran.r-project.org/package=bgms)
 <!-- badges: end -->
 
-# bgms <a href="https://bayesiangraphicalmodeling.com"><img src="inst/bgms_sticker.svg" height="200" align="right" /></a>
 
 **Bayesian analysis of graphical models with binary and ordinal variables**
 
@@ -38,37 +44,37 @@ distribution of the model parameters.
 
 The package has two main entry points:
 
-- `bgm()` – estimates a single network in a one-sample design.  
+- `bgm()` – estimates a single network in a one-sample design.
 - `bgmCompare()` – compares networks between groups in an independent-sample
   design.
 
 ## Effect selection
 
 Both functions support **effect selection** with spike-and-slab priors:
 
-- **Edges in one-sample designs**:  
+- **Edges in one-sample designs**:
   `bgm()` models the presence or absence of edges between variables. Posterior
   inclusion probabilities indicate the plausibility of each edge and can be
   converted into Bayes factors for conditional independence tests [see @MarsmanVandenBerghHaslbeck_2024; @SekulovskiEtAl_2024].
 
-- **Communities/clusters in one-sample designs**:  
+- **Communities/clusters in one-sample designs**:
   `bgm()` can also model community structure. Posterior probabilities for the
   number of clusters quantify the plausibility of clustering solutions and can
   be converted into Bayes factors [see @SekulovskiEtAl_2025].
 
-- **Group differences in independent-sample designs**:  
+- **Group differences in independent-sample designs**:
   `bgmCompare()` models differences in edge weights and category thresholds
   between groups. Posterior inclusion probabilities indicate the plausibility
   of parameter differences and can be converted into Bayes factors for tests of
   parameter equivalence [see @MarsmanWaldorpSekulovskiHaslbeck_2024].
-  
+
 ## Learn more
 
 For worked examples and tutorials, see the package vignettes:
 
-- [Getting Started](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/intro.html)  
-- [Model Comparison](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/comparison.html)  
-- [Diagnostics and Spike-and-Slab Summaries](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/diagnostics.html)  
+- [Getting Started](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/intro.html)
+- [Model Comparison](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/comparison.html)
+- [Diagnostics and Spike-and-Slab Summaries](https://bayesian-graphical-modelling-lab.github.io/bgms/articles/diagnostics.html)
 
 You can also access these directly from R with:
 
@@ -79,23 +85,23 @@ browseVignettes("bgms")
 
 ## Why use Markov Random Fields?
 
-Graphical models or networks have become central in recent psychological and psychometric research [@RobinaughEtAl_2020; @MarsmanRhemtulla_2022_SIintro; @ContrerasEtAl_2019]. Most are **Markov random field (MRF)** models, where the graph structure reflects partial associations between variables [@KindermannSnell1980].  
+Graphical models or networks have become central in recent psychological and psychometric research [@RobinaughEtAl_2020; @MarsmanRhemtulla_2022_SIintro; @ContrerasEtAl_2019]. Most are **Markov random field (MRF)** models, where the graph structure reflects partial associations between variables [@KindermannSnell1980].
 
 In an MRF, a missing edge between two variables implies **conditional independence** given the rest of the network [@Lauritzen2004]. In other words, the remaining variables fully explain away any potential association between the unconnected pair.
 
 ## Why use a Bayesian approach?
 
 When analyzing an MRF, we often want to compare competing hypotheses:
 
-- **Edge presence vs. edge absence** (conditional dependence vs. independence) in one-sample designs.  
+- **Edge presence vs. edge absence** (conditional dependence vs. independence) in one-sample designs.
 - **Parameter difference vs. parameter equivalence** in independent-sample designs.
 
 Frequentist approaches are limited in such comparisons: they can reject a null hypothesis, but they cannot provide evidence *for* it. As a result, when an edge or difference is excluded, it remains unclear whether this reflects true absence or simply insufficient power.
 
 Bayesian inference avoids this problem. Using **inclusion Bayes factors** [@HuthEtAl_2023_intro; @SekulovskiEtAl_2024], we can quantify evidence in both directions:
 
-- **Evidence of edge presence** vs. **evidence of edge absence**, or  
-- **Evidence of parameter difference** vs. **evidence of parameter equivalence**.  
+- **Evidence of edge presence** vs. **evidence of edge absence**, or
+- **Evidence of parameter difference** vs. **evidence of parameter equivalence**.
 
 This makes it possible not only to detect structure and group differences, but also to conclude when there is an *absence of evidence*.