Package: tidyLPA 2.0.1

Joshua M Rosenberg

tidyLPA: Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software

Easily carry out latent profile analysis ("LPA"), determine the correct number of classes based on best practices, and tabulate and plot the results. Provides functionality to estimate commonly-specified models with free means, variances, and covariances for each profile. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Models can be estimated using the free open source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program 'MPlus', via the 'MplusAutomation' package.

Authors:Joshua M Rosenberg [aut, cre], Caspar van Lissa [aut], Jennifer A Schmidt [ctb], Patrick N Beymer [ctb], Daniel Anderson [ctb], Matthew J. Schell [ctb]

tidyLPA_2.0.1.tar.gz
tidyLPA_2.0.1.zip(r-4.7)tidyLPA_2.0.1.zip(r-4.6)tidyLPA_2.0.1.zip(r-4.5)
tidyLPA_2.0.1.tgz(r-4.6-any)tidyLPA_2.0.1.tgz(r-4.5-any)
tidyLPA_2.0.1.tar.gz(r-4.7-any)tidyLPA_2.0.1.tar.gz(r-4.6-any)
tidyLPA_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
tidyLPA/json (API)
NEWS

# Install 'tidyLPA' in R:
install.packages('tidyLPA', repos = c('https://cjvanlissa.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/data-edu/tidylpa/issues

Pkgdown/docs site:https://data-edu.github.io

Datasets:
  • curry_mac - Simulated MAC data
  • empathy - Simulated empathy data
  • id_edu - Simulated identity data
  • pisaUSA15 - Student questionnaire data with four variables from the 2015 PISA for students in the United States

On CRAN:

Conda:

7.58 score 1 stars 1 packages 213 scripts 7.5k downloads 19 mentions 13 exports 114 dependencies

Last updated from:64906abfd7. Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK232
source / vignettesOK355
linux-release-x86_64OK267
macos-release-arm64OK139
macos-oldrel-arm64OK116
windows-develOK209
windows-releaseOK164
windows-oldrelOK155
wasm-releaseOK198

Exports:%>%AHPcalc_lrtcompare_solutionsestimate_profilesget_dataget_estimatesget_fitplot_bivariateplot_densityplot_profilespomssingle_imputation

Dependencies:abindaskpassbackportsbootbroomcarcarDatacheckmateclicodacodetoolscolorspaceCompQuadFormcowplotcpp11crayoncurldata.tabledbscanDerivdigestdoBydplyrfarverfastDummiesforecastFormulafracdifffuturefuture.applygenericsggplot2globalsglueGPArotationgsubfngtablehmshttrigraphisobandjsonlitelabelinglatticelavaanlifecyclelistenvlme4lmtestmagrittrMASSMatrixMatrixModelsmclustmgcvmicrobenchmarkmimeminqamnormtmodelrMplusAutomationmvtnormnlmenloptrnnetnonnest2numDerivopensslpanderparallellypbivnormpbkrtestpillarpkgconfigplyrprettyunitsprogressprogressrprotopsychpurrrquadprogquantregR6RANNrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7sandwichscalesSparseMstringistringrsurvivalsystexregtibbletidyrtidyselecttidySEMtimeDateurcautf8vctrsviridisLitewithrxtablezoo

Benchmarking mclust and MPlus

Vignette Author

Rendered frombenchmarking-mclust-and-mplus.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2021-06-03
Started: 2019-07-19

Introduction to tidyLPA

Joshua M. Rosenberg

Rendered fromIntroduction_to_tidyLPA.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2021-06-03
Started: 2017-10-28