Package: statMatchLCM 1.2

Alicja Wolny-Dominiak
statMatchLCM: Statistical Matching Using Latent Class Models
Tools for statistical matching based on latent class models. The package implements statistical matching procedures based on latent class models. It allows researchers to perform data integration when no unique identifiers are available by modeling the joint distribution of variables through latent categorical structures. The package supports estimation of latent class models, probabilistic matching between donor and recipient data sets, and generation of synthetic linked data under uncertainty. It is particularly useful in survey research and data fusion applications where combining information from multiple sources is required while preserving statistical properties and accounting for measurement error and missing data mechanisms.
Authors:
statMatchLCM_1.2.tar.gz
statMatchLCM_1.2.tar.gz(r-4.7-any)statMatchLCM_1.2.tar.gz(r-4.6-any)
statMatchLCM_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
statMatchLCM/json (API)
| # Install 'statMatchLCM' in R: |
| install.packages('statMatchLCM', 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:49cd7de7d6. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 160 | ||
| source / vignettes | OK | 177 | ||
| linux-release-x86_64 | OK | 153 | ||
| wasm-release | OK | 123 |
Exports:datAB_to_SMfact_to_numnum_to_factsm_qualitysma_step1sma1_step2sma2_step2sma3_step2smc_step1smc1_step2smc2_step2smc3_step2
Dependencies:clicpp11DBIdplyrfarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclelpSolvemagrittrMatrixminqamitoolsnnetnumDerivpillarpkgconfigproxyR6RColorBrewerRcppRcppArmadillorlangS7scalesStatMatchsurveysurvivaltibbletidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Dataset datA | datA |
| Create stacked A/B dataset for MDC | datAB_to_SM |
| Dataset datB | datB |
| Convert factor/character variables to numeric with mapping | fact_to_num |
| Convert numeric codes back to factor | num_to_fact |
| Quality assessment of synthetic Z1 | sm_quality |
| SMA step 1: selection of best imputed dataset | sma_step1 |
| SMA1 – Nearest-neighbour hot deck on shared variables | sma1_step2 |
| SMA2 – Hot deck on fitted multinomial probabilities | sma2_step2 |
| SMA3 – Multinomial simulation approach | sma3_step2 |
| SMC step 1: selection of best imputed dataset | smc_step1 |
| SMC1 - Nearest-neighbour hot deck on original variables | smc1_step2 |
| SMC2 – Hot deck on fitted multinomial probabilities | smc2_step2 |
| SMC3 – Multinomial simulation approach | smc3_step2 |