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✨
DESCRIPTION |NEWS
card.svg |card.png
tidyLPA/json (API)

# 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:

7.59 score 1 stars 1 packages 219 scripts 7.4k downloads 19 mentions 13 exports 114 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK260
source / vignettesOK341
linux-release-x86_64OK285
macos-release-arm64OK105
macos-oldrel-arm64OK191
windows-develOK203
windows-releaseOK168
windows-oldrelOK180
wasm-releaseOK215

Exports:%>%AHPcalc_lrtcompare_solutionsestimate_profilesget_dataget_estimatesget_fitplot_bivariateplot_densityplot_profilespomssingle_imputation

Dependencies:abindaskpassbackportsbootbroomcarcarDatacheckmateclicodacodetoolscolorspaceCompQuadFormcowplotcpp11crayoncurldata.tabledbscanDerivdigestdoBydplyrfarverfastDummiesforecastFormulafracdifffuturefuture.applygenericsggplot2globalsglueGPArotationgsubfngtablehmshttrigraphisobandjsonlitelabelinglatticelavaanlifecyclelistenvlme4lmtestmagrittrMASSMatrixMatrixModelsmclustmgcvmicrobenchmarkmimeminqamnormtmodelrMplusAutomationmvtnormnlmenloptrnnetnonnest2numDerivopensslpanderparallellypbivnormpbkrtestpillarpkgconfigplyrprettyunitsprogressprogressrprotopsychpurrrquadprogquantregR6RANNrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7sandwichscalesSparseMstringistringrsurvivalsystexregtibbletidyrtidyselecttidySEMtimeDateurcautf8vctrsviridisLitewithrxtablezoo

Benchmarking mclust and MPlus
Purpose | Using mclust | Just one fit | Using MPlus | Many fits
Rendered frombenchmarking-mclust-and-mplus.Rmdusingknitr::rmarkdown

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

Introduction to tidyLPA
Background on Latent Profile Analysis (LPA) | Description of the goals of tidyLPA | Software approach to carrying out LPA: Interface to mclust (and to MPlus) | Example | Installation | Mclust | Mplus | Comparing a wide range of solutions | Passing additional arguments | More information on model specifications | Model specification | 1. Equal variances, and covariances fixed to 0 (model 1) | 2. Varying variances and covariances fixed to 0 (model 2) | 3. Equal variances and equal covariances (model 3) | 4. Varying means, varying variances, and equal covariances (model 4) | 5. Varying means, equal variances, and varying covariances (model 5) | 6. Varying variances and varying covariances (model 6) | Other functionality | Getting estimates | Getting data | Getting fit statistics | Acknowledgments
Rendered fromIntroduction_to_tidyLPA.Rmdusingknitr::rmarkdown

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