Package: DLCA 1.1
Daniel W. van der Palm
DLCA: Divisive Latent Class Analysis
Provides algorithms for estimating divisive and standard latent class models. The divisive latent class method follows van der Palm, van der Ark and Vermunt (2016) <doi:10.1007/s00357-016-9195-5>. Both algorithms use expectation-maximization and Newton-Raphson optimization and are implemented in 'C++' for speed through 'Rcpp'.
Authors:
DLCA_1.1.tar.gz
DLCA_1.1.zip(r-4.7-x86_64)DLCA_1.1.zip(r-4.7-arm64)DLCA_1.1.zip(r-4.6-x86_64)DLCA_1.1.zip(r-4.6-arm64)
DLCA_1.1.tar.gz(r-4.7-arm64)DLCA_1.1.tar.gz(r-4.7-x86_64)DLCA_1.1.tar.gz(r-4.6-arm64)DLCA_1.1.tar.gz(r-4.6-x86_64)
DLCA_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DLCA/json (API)
| # Install 'DLCA' in R: |
| install.packages('DLCA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:b1ffb77f49. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 112 | ||
| linux-devel-x86_64 | OK | 115 | ||
| source / vignettes | OK | 233 | ||
| linux-release-arm64 | OK | 105 | ||
| linux-release-x86_64 | OK | 113 | ||
| windows-devel-arm64 | OK | 233 | ||
| windows-devel-x86_64 | OK | 157 | ||
| windows-release-arm64 | OK | 241 | ||
| windows-release-x86_64 | OK | 116 | ||
| wasm-release | OK | 116 |
Exports:CalculateFitStatisticscheckDLCAInstallDLCgetUniqueIndependenceLogLikelihoodLCALCAWarmStartprepareDatarunDLCArunLCARunLCArunLCAPreparedRunSingleClassLCArunSLCAsimDatasimDataLCMsimDataLCMFromParametersSLCASequence
Dependencies:Rcpp
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Inspect a DLCA Installation | checkDLCAInstall |
| The C++ code to estimate a divisive latent class model | DLC |
| Low-Level DLCA Native Interfaces | CalculateFitStatistics IndependenceLogLikelihood LCAWarmStart RunLCA RunSingleClassLCA SLCASequence |
| The C++ code to obtain the unique response patterns | getUnique |
| The C++ code to estimate a latent class model | LCA |
| Prepares the data to avoid estimation issues and to increase computation speed | prepareData |
| Estimate a divisive latent class model | runDLCA |
| Estimate a latent class model | runLCA |
| Estimate a Latent Class Model from Prepared Data | runLCAPrepared |
| Estimate a Sequence of Latent Class Models | runSLCA |
| Simulate data to try out the runDLCA or runLCA function | simData |
| Simulate Data from a Latent Class Model | simDataLCM simDataLCMFromParameters |