<b>BayesLCA</b>: An<i>R</i>Package for Bayesian Latent Class Analysis

White, Arthur

doi:10.18637/jss.v061.i13

Cited by 60 publications

(54 citation statements)

References 31 publications

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“…Free programs are LEM (Vermunt, 1997) and various packages developed for R, a free software environment for statistical computing. These include the packages poLCA (Linzer & Lewis, 2011, 2013, MCLUST (Fraley, Raftery, Murphy, & Scrucca, 2012), depmixS4 (Visser & Speekenbrink, 2010), mixtools (Benaglia, Chauveau, Hunter, & Young, 2009), and BayesLCA (White & Murphy, 2014).…”

Section: Software Implementationmentioning

confidence: 99%

“…The mixtools-package allows nonparametric modeling to circumvent assumptions of a normal distribution of indicator variables (Benaglia et al, 2009). In the BayesLCA package, Bayesian estimation is applied which does not depend on asymptotic assumptions and allows incorporating prior information into the model to ease parameter estimation and inference in small samples (White & Murphy, 2014). Bayesian estimation is also possible in the Mplus package, albeit currently only for cross-sectional (LCA/LPA) models without covariates (Muthén & Muthén, 2017).…”

Section: Software Implementationmentioning

confidence: 99%

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Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

Hickendorff

Edelsbrunner

McMullen

et al. 2018

Learning and Individual Differences

197

118

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Highlights• learning is often multidimensional, heterogeneous, and discontinuous• traditional statistical analyses are limited in capturing this complexity• latent class and latent profile models identify subgroups of learners• latent transition models characterize discontinuous, non-linear, learning paths• these models contribute to our understanding of learning and individual differences 3 Informative Tools for Characterizing Individual Differences in Learning: Latent Class, Latent Profile, and Latent Transition AnalysisThis article gives an introduction to latent class, latent profile, and latent transition models for researchers interested in investigating individual differences in learning and development. The models allow analyzing how the observed heterogeneity in a group (e.g., individual differences in conceptual knowledge) can be traced back to underlying homogeneous subgroups (e.g., learners differing systematically in their developmental phases). The estimated parameters include a characteristic response pattern for each subgroup, and, in the case of longitudinal data, the probabilities of transitioning from one subgroup to another over time. This article describes the steps involved in using the models, gives practical examples, and discusses limitations and extensions. Overall, the models help to characterize heterogeneous learner populations, multidimensional learning outcomes, nonlinear learning pathways, and changing relations between learning processes. The application of these models can therefore make a substantial contribution to our understanding of learning and individual differences.

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Section: Software Implementationmentioning

confidence: 99%

Section: Software Implementationmentioning

confidence: 99%

Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

Hickendorff

Edelsbrunner

McMullen

et al. 2018

Learning and Individual Differences

197

118

View full text Add to dashboard Cite

show abstract

“…R packages implementing the LCA model for clustering categorical data are BayesLCA (White and Murphy, 2014), poLCA (Linzer and Lewis, 2011) and flexmix (Leisch, 2004); package e1071 (Meyer et al, 2017) contains function lca for fitting the LCA model on binary data.…”

Section: Latent Class Analysis Modelmentioning

confidence: 99%

Variable selection methods for model-based clustering

Fop¹,

Murphy²

2018

Statist. Surv.

Self Cite

100

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Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the modelbased clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received a lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for variable selection in model-based clustering. Existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples. Fop, M. and Murphy, T. B./Variable Selection Methods 2 Fop, M. and Murphy, T. B./Variable Selection MethodsFig 1: Local and global independence assumptions. In the example, z is the group membership variable, X1, X2 and X3 are relevant clustering variables, while X4 and X5 are irrelevant and not related to z. Under the local independence assumption there are no edges among the relevant variables. Under the global independence assumption there is no edge between the set of relevant variables and the set of irrelevant ones.

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“…In addition many of these classes had only a few responses. The number of change classes was reduced to five using Bayesian Latent Class Analysis (BLCA; White and Murphy 2014). BLCA seeks to find latent classes (clusters) in sets of binomial data.…”

Section: Predicting Action Modeling Sr Structurementioning

confidence: 99%

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

Lynam¹

2016

E&S

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BayesLCA: AnRPackage for Bayesian Latent Class Analysis

Cited by 60 publications

References 31 publications

Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis

Variable selection methods for model-based clustering

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

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