APA style referencing Readme

Author: MaartenMarsman

README.md

@@ -3,15 +3,15 @@
 
 The `R` package <strong>bgms</strong> provides tools for Bayesian
 analysis of the ordinal Markov random field, a graphical model
-describing a network of binary and/or ordinal variables (Marsman, van
-den Bergh, and Haslbeck 2025). A pseudolikelihood is used to approximate
-the likelihood of the graphical model, and Markov chain Monte Carlo
-methods are used to simulate from the corresponding pseudoposterior
-distribution of the graphical model parameters.
+describing a network of binary and/or ordinal variables (Marsman et al.,
+2025). A pseudolikelihood is used to approximate the likelihood of the
+graphical model, and Markov chain Monte Carlo methods are used to
+simulate from the corresponding pseudoposterior distribution of the
+graphical model parameters.
 
 The <strong>bgm</strong> function can be used for a one-sample design
 and the <strong>bgmCompare</strong> function can be used for an
-independent-sample design (see Marsman et al. 2024). Both functions can
+independent-sample design (see Marsman et al., 2024). Both functions can
 model the selection of effects. In one-sample designs, the
 <strong>bgm</strong> function models the presence or absence of edges
 between pairs of variables in the network. The estimated posterior
@@ -23,8 +23,8 @@ presence or absence of communities or clusters of variables in the
 network. The estimated posterior probability distribution of the number
 of clusters indicates how plausible it is that a network with the
 corresponding number of clusters produced the observed data, and can be
-converted into a Bayes factor test for clustering (see Sekulovski et al.
-2025).
+converted into a Bayes factor test for clustering (see Sekulovski et
+al., 2025).
 
 In an independent-sample design, the <strong>bgmCompare</strong>
 function models the selection of group differences in edge weights and
@@ -37,13 +37,13 @@ for parameter equivalence.
 ## Why use Markov Random Fields?
 
 Multivariate analysis using graphical models has received much attention
-in the recent psychological and psychometric literature (Robinaugh et
-al. 2020; Marsman and Rhemtulla 2022; Contreras et al. 2019). Most of
+in the recent psychological and psychometric literature (Contreras et
+al., 2019; Marsman & Rhemtulla, 2022; Robinaugh et al., 2020). Most of
 these graphical models are Markov Random Field (MRF) models, whose graph
 structure reflects the partial associations between variables
-(Kindermann and Snell 1980). In these models, a missing edge between two
+(Kindermann & Snell, 1980). In these models, a missing edge between two
 variables in the network implies that these variables are independent,
-given the remaining variables (Lauritzen 2004). In other words, the
+given the remaining variables (Lauritzen, 2004). In other words, the
 remaining variables of the network fully account for the potential
 association between the unconnected variables.
 
@@ -73,8 +73,8 @@ parameter between the different groups or because we do not have enough
 data to reject the null hypothesis of parameter equivalence.
 
 To avoid this problem, we will advocate a Bayesian approach using Bayes
-factors. In one-sample designs, the inclusion Bayes factor (Huth et al.
-2023; Sekulovski et al. 2024) allows us to quantify how much the data
+factors. In one-sample designs, the inclusion Bayes factor (Huth et al.,
+2023; Sekulovski et al., 2024) allows us to quantify how much the data
 support both conditional dependence -<em>evidence of edge
 presence</em> - or conditional independence -<em>evidence of edge
 absence</em>. It also allows us to conclude that there is limited
@@ -98,94 +98,92 @@ remotes::install_github("MaartenMarsman/bgms")
 ## References
 
 <div id="refs" class="references csl-bib-body hanging-indent"
-entry-spacing="0">
+entry-spacing="0" line-spacing="2">
 
 <div id="ref-ContrerasEtAl_2019" class="csl-entry">
 
-Contreras, A., I. Nieto, C. Valiente, R. Espinosa, and C. Vazquez. 2019.
-“The Study of Psychopathology from the Network Analysis Perspective: A
-Systematic Review.” *Psychotherapy and Psychosomatics* 88: 71–83.
-<https://doi.org/10.1159/000497425>.
+Contreras, A., Nieto, I., Valiente, C., Espinosa, R., & Vazquez, C.
+(2019). The study of psychopathology from the network analysis
+perspective: A systematic review. *Psychotherapy and Psychosomatics*,
+*88*, 71–83. <https://doi.org/10.1159/000497425>
 
 </div>
 
 <div id="ref-HuthEtAl_2023_intro" class="csl-entry">
 
-Huth, K., J. de Ron, A. E. Goudriaan, K. Luigjes, R. Mohammadi, R. J.
-van Holst, E.-J. Wagenmakers, and M. Marsman. 2023. “Bayesian Analysis
-of Cross-Sectional Networks: A Tutorial in R and JASP.” *Advances in
-Methods and Practices in Psychological Science* 6: 1–18.
-<https://doi.org/10.1177/25152459231193334>.
+Huth, K., de Ron, J., Goudriaan, A. E., Luigjes, K., Mohammadi, R., van
+Holst, R. J., Wagenmakers, E.-J., & Marsman, M. (2023). Bayesian
+analysis of cross-sectional networks: A tutorial in R and JASP.
+*Advances in Methods and Practices in Psychological Science*, *6*, 1–18.
+<https://doi.org/10.1177/25152459231193334>
 
 </div>
 
 <div id="ref-KindermannSnell1980" class="csl-entry">
 
-Kindermann, R., and J. L. Snell. 1980. *Markov Random Fields and Their
-Applications*. Vol. 1. Contemporary Mathematics. Providence: American
-Mathematical Society.
+Kindermann, R., & Snell, J. L. (1980). *Markov random fields and their
+applications* (Vol. 1). American Mathematical Society.
 
 </div>
 
 <div id="ref-Lauritzen2004" class="csl-entry">
 
-Lauritzen, S. L.. 2004. *Graphical Models*. Oxford: Oxford University
-Press.
+Lauritzen, S. L. (2004). *Graphical models*. Oxford University Press.
 
 </div>
 
 <div id="ref-MarsmanRhemtulla_2022_SIintro" class="csl-entry">
 
-Marsman, M., and M. Rhemtulla. 2022. “Guest Editors’ Introduction to the
-Special Issue ‘Network Psychometrics in Action’: Methodological
-Innovations Inspired by Empirical Problems.” *Psychometrika* 87: 1–11.
-<https://doi.org/10.1007/s11336-022-09861-x>.
+Marsman, M., & Rhemtulla, M. (2022). Guest editors’ introduction to the
+special issue “network psychometrics in action”: Methodological
+innovations inspired by empirical problems. *Psychometrika*, *87*, 1–11.
+<https://doi.org/10.1007/s11336-022-09861-x>
 
 </div>
 
 <div id="ref-MarsmanVandenBerghHaslbeck_2024" class="csl-entry">
 
-Marsman, M., D. van den Bergh, and J. M. B. Haslbeck. 2025. “Bayesian
-Analysis of the Ordinal Markov Random Field.” *Psychometrika* 90:
+Marsman, M., van den Bergh, D., & Haslbeck, J. M. B. (2025). Bayesian
+analysis of the ordinal Markov random field. *Psychometrika*, *90*,
 146--182.
 
 </div>
 
 <div id="ref-MarsmanWaldorpSekulovskiHaslbeck_2024" class="csl-entry">
 
-Marsman, M., L. J. Waldorp, N. Sekulovski, and J. M. B. Haslbeck. 2024.
-“A Bayesian Independent Samples $t$ Test for Parameter Differences in
-Networks of Binary and Ordinal Variables.” *Retrieved from
+Marsman, M., Waldorp, L. J., Sekulovski, N., & Haslbeck, J. M. B.
+(2024). A bayesian independent samples $t$ test for parameter
+differences in networks of binary and ordinal variables. *Retrieved from
 Https://Osf.io/Preprints/Osf/F4pk9*.
 
 </div>
 
 <div id="ref-RobinaughEtAl_2020" class="csl-entry">
 
-Robinaugh, D. J., R. H. A. Hoekstra, E. R. Toner, and D. Borsboom. 2020.
-“The Network Approach to Psychopathology: A Review of the Literature
-2008–2018 and an Agenda for Future Research.” *Psychological Medicine*
-50: 353–66. <https://doi.org/10.1017/S0033291719003404>.
+Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D.
+(2020). The network approach to psychopathology: A review of the
+literature 2008–2018 and an agenda for future research. *Psychological
+Medicine*, *50*, 353–366. <https://doi.org/10.1017/S0033291719003404>
 
 </div>
 
 <div id="ref-SekulovskiEtAl_2025" class="csl-entry">
 
-Sekulovski, N., G. Arena, J. M. B. Haslbeck, K. B. S. Huth, N. Friel,
-and M. Marsman. 2025. “A Stochastic Block Prior for Clustering in
-Graphical Models.” *Retrieved from
+Sekulovski, N., Arena, G., Haslbeck, J. M. B., Huth, K. B. S., Friel,
+N., & Marsman, M. (2025). A stochastic block prior for clustering in
+graphical models. *Retrieved from
 <a href="https://osf.io/preprints/psyarxiv/29p3m_v1"
 class="uri">Https://Osf.io/Preprints/Psyarxiv/29p3m_v1</a>*.
 
 </div>
 
 <div id="ref-SekulovskiEtAl_2024" class="csl-entry">
 
-Sekulovski, N., S. Keetelaar, K. B. S. Huth, Eric-Jan Wagenmakers, R.
-van Bork, D. van den Bergh, and M. Marsman. 2024. “Testing Conditional
-Independence in Psychometric Networks: An Analysis of Three Bayesian
-Methods.” *Multivariate Behavioral Research* 59: 913–33.
-<https://doi.org/10.1080/00273171.2024.2345915>.
+Sekulovski, N., Keetelaar, S., Huth, K. B. S., Wagenmakers, E.-J., van
+Bork, R., van den Bergh, D., & Marsman, M. (2024). Testing conditional
+independence in psychometric networks: An analysis of three Bayesian
+methods. *Multivariate Behavioral Research*, *59*, 913–933.
+<https://doi.org/10.1080/00273171.2024.2345915>
 
 </div>
 

Readme.Rmd

@@ -1,6 +1,7 @@
 ---
 output: github_document
 bibliography: inst/REFERENCES.bib
+csl: inst/apa.csl
 ---
 
 ```{r, echo = FALSE, message=F}