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:

13 exports 55 stars 4.40 score 127 dependencies 19 mentions 100 scripts 2.4k downloads

Last updated 7 months agofrom:b303727f63. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-winNOTEAug 28 2024
R-4.5-linuxNOTEAug 28 2024
R-4.4-winNOTEAug 28 2024
R-4.4-macNOTEAug 28 2024
R-4.3-winOKAug 28 2024
R-4.3-macOKAug 28 2024

Exports:%>%AHPcalc_lrtcompare_solutionsestimate_profilesget_dataget_estimatesget_fitplot_bivariateplot_densityplot_profilespomssingle_imputation

Dependencies:abindaskpassbackportsbainbayesplotBHblavaanbootbroomcallrcarcarDatacheckmateclicodacodetoolscolorspaceCompQuadFormcowplotcpp11curldata.tabledbscanDerivdescdigestdistributionaldoBydplyrfansifarverfastDummiesfuturefuture.applygenericsggplot2ggridgesglobalsglueGPArotationgridExtragsubfngtablehttrigraphinlineisobandjsonlitelabelinglatticelavaanlifecyclelistenvlme4loomagrittrMASSMatrixMatrixModelsmatrixStatsmclustmgcvmicrobenchmarkmimeminqamnormtmodelrMplusAutomationmunsellmvtnormnlmenloptrnnetnonnest2numDerivOpenMxopensslpanderparallellypbivnormpbkrtestpillarpkgbuildpkgconfigplyrposteriorprocessxprogressrprotopspsychpurrrquadprogquantregQuickJSRR6RANNRColorBrewerRcppRcppEigenRcppParallelreshape2rlangrpfrstanrstantoolssandwichscalesSparseMStanHeadersstringistringrsurvivalsystensorAtexregtibbletidyrtidyselecttidySEMtmvnsimutf8vctrsviridisLitewithrxtablezoo

Benchmarking mclust and MPlus

Vignette Author

Rendered frombenchmarking-mclust-and-mplus.Rmdusingknitr::rmarkdownon Aug 28 2024.

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

Introduction to tidyLPA

Joshua M. Rosenberg

Rendered fromIntroduction_to_tidyLPA.Rmdusingknitr::rmarkdownon Aug 28 2024.

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