This package is considered a duplicate. The official version of this package is found at:https://samleebyu.r-universe.dev/tseLCA

Package: tseLCA 2.1.1

Sam Lee

tseLCA: Three-Step Estimation for Latent Class Analysis

Bias-adjusted three-step estimation of latent class models with covariates and distal outcomes. The latent class measurement model is estimated first, with 'multilevLCA' (Lyrvall et al., 2025) <doi:10.1080/00273171.2025.2473935>, and held fixed; observations are then classified; and the classes are related to covariates and distal outcomes with the maximum likelihood correction of Vermunt (2010) <doi:10.1093/pan/mpq025> and Bakk, Tekle and Vermunt (2013) <doi:10.1177/0081175012470644>, or the correction of Bolck, Croon and Hagenaars (2004) <doi:10.1093/pan/mph001>. Standard errors account for the uncertainty of the measurement model (Bakk, Oberski and Vermunt, 2014) <doi:10.1093/pan/mpu003>. Includes class enumeration, modal and proportional class assignment, covariate formulas, Gaussian, Poisson, binomial, and multinomial distal outcomes, the two-step estimator of Bakk and Kuha (2018) <doi:10.1007/s11336-017-9592-7>, measurement models applied to new samples, and full-information maximum likelihood for missing indicators, standard methods for fitted models, and a data-generating process replicating the simulation design of Bakk and Kuha (2018).

Authors:Sam Lee [aut, cre, cph], Jay Goodliffe [aut, cph]

tseLCA_2.1.1.tar.gz
tseLCA_2.1.1.zip(r-4.7-any)tseLCA_2.1.1.zip(r-4.6-any)
tseLCA_2.1.1.tar.gz(r-4.7-any)tseLCA_2.1.1.tar.gz(r-4.6-any)
tseLCA_2.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tseLCA/json (API)

# Install 'tseLCA' in R:

install.packages('tseLCA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/samleebyu/tselca/issues

Pkgdown/docs site:https://samleebyu.github.io

On CRAN:

3.48 score 500 downloads 33 exports 87 dependencies

Last updated from:7c6c0f83b6. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel	OK	169
source / vignettes	OK	233
linux-release	OK	174
windows-devel	OK	118
windows-release	OK	144
wasm-release	OK	121

Exports:as_tse_lca best_model bk2018_params class_sizes classes classification covariate distal draw_classes draw_classes_given_Zp draw_indicators draw_Zo draw_Zp fitZ_from_fit0 fitZ_from_multiLCA generate_all_conditions generate_data item_probs lca_step1 lca_step1_startval make_rho measurement mnl_probs omnibus_test posterior three_step tse_classify tse_control tse_covariate tse_distal tse_lca tse_twostep tseLCA

Dependencies:base64enc bit bit64 bslib cachem class classInt cli clipr cluster clustMixType codetools combinat commonmark cpp11 crayon digest dplyr e1071 fastmap fontawesome forcats foreach Formula fs generics glue haven highr hms htmltools httpuv iterators jquerylib jsonlite KernSmooth klaR labelled later lifecycle magrittr MASS mclust memoise mime miniUI multilevLCA otel pillar pkgconfig pracma prettyunits progress promises proxy purrr questionr R.cache R.methodsS3 R.oo R.utils R6 rappdirs RColorBrewer Rcpp RcppArmadillo readr rlang rprojroot rstudioapi sass shiny sourcetools stringi stringr styler tibble tictoc tidyr tidyselect tzdb utf8 vctrs vroom withr xfun xtable

tseLCA Workflow

Rendered fromtseLCA-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-10-31
Started: 2026-07-11

Help page	Topics
Wald tests of covariate terms	anova.tseLCA_covariate
Use a measurement model with given parameters (Step 1)	as_tse_lca
Class enumeration results	as.data.frame.tseLCA_select best_model best_model.tseLCA_select plot.tseLCA_select print.tseLCA_select [[.tseLCA_select
Default population parameters for the Bakk & Kuha (2018) simulation	bk2018_params
Class sizes and item-response probabilities of the measurement model	class_sizes class_sizes.tseLCA item_probs item_probs.tseLCA
Coefficients of a fitted tseLCA model	coef.tseLCA_measurement coef.tseLCA_structural
Draw latent class memberships from their marginal distribution	draw_classes
Draw latent classes conditional on the covariate (scenario "covariate")	draw_classes_given_Zp
Draw binary indicators given true class memberships	draw_indicators
Draw a continuous distal outcome given true class memberships (scenario "distal")	draw_Zo
Draw the covariate Zp ~ Uniform{1, 2, 3, 4, 5}	draw_Zp
Estimate covariate effects with measurement parameters fixed (two-step EM)	fitZ_from_fit0
Estimate two-step covariate model with multilevLCA (optional reference path)	fitZ_from_multiLCA
Generate datasets for all 18 conditions in the simulation design	generate_all_conditions
Generate one dataset following the Bakk & Kuha (2018) simulation design	generate_data
Fit the LCA measurement model (Step 1)	lca_step1
Log-likelihood, number of observations, and information criteria	logLik.tseLCA nobs.tseLCA
Build the item-response probability matrix for the simulation	make_rho
Components of a fitted tseLCA model	classification classification.tseLCA covariate covariate.tseLCA distal distal.tseLCA measurement measurement.tseLCA
Compute multinomial logistic class probabilities given covariates	mnl_probs
Omnibus Wald test of class equality for a distal outcome	omnibus_test omnibus_test.tseLCA_both omnibus_test.tseLCA_distal
Plot item-response probability profiles for a tseLCA model	plot.tseLCA
Posterior class-membership probabilities and modal class assignments	classes classes.tseLCA posterior posterior.tseLCA
Class-membership probabilities from a covariate model	predict.tseLCA_covariate
Class membership predictions from a measurement model	fitted.tseLCA_measurement predict.tseLCA_measurement
Change the reference class of a covariate model	relevel.tseLCA_covariate
Summarize a fitted tseLCA model	coef.summary.tseLCA_structural print.summary.tseLCA_measurement print.summary.tseLCA_structural print.tseLCA_measurement print.tseLCA_structural summary.tseLCA_measurement summary.tseLCA_structural
Three-step LCA estimation with covariates and/or distal outcomes	three_step
Assign observations to latent classes (Step 2)	print.tseLCA_classify tse_classify
Estimation settings for tseLCA models	tse_control
Relate latent classes to covariates (Step 3)	tse_covariate
Relate latent classes to a distal outcome (Step 3)	tse_distal
Fit a latent class measurement model (Step 1)	tse_lca
Two-step estimates of covariate effects	tse_twostep
Three-step latent class analysis in one call	tseLCA
Variance-covariance matrix of a fitted tseLCA model	vcov.tseLCA_measurement vcov.tseLCA_structural

Package: tseLCA 2.1.1

tseLCA: Three-Step Estimation for Latent Class Analysis

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Help Manual

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