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-4.5)tidyLPA_2.0.1.zip(r-4.4)tidyLPA_2.0.1.zip(r-4.3)
tidyLPA_2.0.1.tgz(r-4.4-any)tidyLPA_2.0.1.tgz(r-4.3-any)
tidyLPA_2.0.1.tar.gz(r-4.5-noble)tidyLPA_2.0.1.tar.gz(r-4.4-noble)
tidyLPA_2.0.1.tgz(r-4.4-emscripten)tidyLPA_2.0.1.tgz(r-4.3-emscripten)
tidyLPA.pdf |tidyLPA.html✨
tidyLPA/json (API)
NEWS

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

Peer review:

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

Pkgdown 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:

8.87 score 57 stars 117 scripts 1.8k downloads 19 mentions 13 exports 131 dependencies

Last updated 12 months agofrom:b303727f63. Checks:3 OK, 4 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 25 2025
R-4.5-winNOTEJan 25 2025
R-4.5-linuxNOTEJan 25 2025
R-4.4-winNOTEJan 25 2025
R-4.4-macNOTEJan 25 2025
R-4.3-winOKJan 25 2025
R-4.3-macOKJan 25 2025

Exports:%>%AHPcalc_lrtcompare_solutionsestimate_profilesget_dataget_estimatesget_fitplot_bivariateplot_densityplot_profilespomssingle_imputation

Dependencies:abindaskpassbackportsbainbayesplotBHblavaanbootbroomcallrcarcarDatacheckmateclicodacodetoolscolorspaceCompQuadFormcowplotcpp11curldata.tabledbscanDerivdescdigestdistributionaldoBydplyrfansifarverfastDummiesFormulafuturefuture.applygenericsggplot2ggridgesglobalsglueGPArotationgridExtragsubfngtablehttrigraphinlineisobandjsonlitelabelinglatticelavaanlifecyclelistenvlme4loomagrittrMASSMatrixMatrixModelsmatrixStatsmclustmgcvmicrobenchmarkmimeminqamnormtmodelrMplusAutomationmunsellmvtnormnlmenloptrnnetnonnest2numDerivOpenMxopensslpanderparallellypbivnormpbkrtestpillarpkgbuildpkgconfigplyrposteriorprocessxprogressrprotopspsychpurrrquadprogquantregQuickJSRR6RANNrbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreformulasreshape2rlangrpfrstanrstantoolssandwichscalesSparseMStanHeadersstringistringrsurvivalsystensorAtexregtibbletidyrtidyselecttidySEMtmvnsimutf8vctrsviridisLitewithrxtablezoo

Benchmarking mclust and MPlus

Vignette Author

Rendered frombenchmarking-mclust-and-mplus.Rmdusingknitr::rmarkdownon Jan 25 2025.

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

Introduction to tidyLPA

Joshua M. Rosenberg

Rendered fromIntroduction_to_tidyLPA.Rmdusingknitr::rmarkdownon Jan 25 2025.

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