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

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:

8.95 score 56 stars 105 scripts 2.6k downloads 19 mentions 13 exports 128 dependencies

Last updated 9 months agofrom:b303727f63. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winNOTESep 27 2024
R-4.5-linuxNOTEOct 27 2024
R-4.4-winNOTEOct 27 2024
R-4.4-macNOTEOct 27 2024
R-4.3-winOKOct 27 2024
R-4.3-macOKOct 27 2024

Exports:%>%AHPcalc_lrtcompare_solutionsestimate_profilesget_dataget_estimatesget_fitplot_bivariateplot_densityplot_profilespomssingle_imputation

Dependencies:abindaskpassbackportsbainbayesplotBHblavaanbootbroomcallrcarcarDatacheckmateclicodacodetoolscolorspaceCompQuadFormcowplotcpp11curldata.tabledbscanDerivdescdigestdistributionaldoBydplyrfansifarverfastDummiesFormulafuturefuture.applygenericsggplot2ggridgesglobalsglueGPArotationgridExtragsubfngtablehttrigraphinlineisobandjsonlitelabelinglatticelavaanlifecyclelistenvlme4loomagrittrMASSMatrixMatrixModelsmatrixStatsmclustmgcvmicrobenchmarkmimeminqamnormtmodelrMplusAutomationmunsellmvtnormnlmenloptrnnetnonnest2numDerivOpenMxopensslpanderparallellypbivnormpbkrtestpillarpkgbuildpkgconfigplyrposteriorprocessxprogressrprotopspsychpurrrquadprogquantregQuickJSRR6RANNRColorBrewerRcppRcppEigenRcppParallelreshape2rlangrpfrstanrstantoolssandwichscalesSparseMStanHeadersstringistringrsurvivalsystensorAtexregtibbletidyrtidyselecttidySEMtmvnsimutf8vctrsviridisLitewithrxtablezoo

Benchmarking mclust and MPlus

Vignette Author

Rendered frombenchmarking-mclust-and-mplus.Rmdusingknitr::rmarkdownon Oct 27 2024.

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

Introduction to tidyLPA

Joshua M. Rosenberg

Rendered fromIntroduction_to_tidyLPA.Rmdusingknitr::rmarkdownon Oct 27 2024.

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