Package: CLCM 0.1.1

CLCM: Estimate Confirmatory Latent Class Models

Estimate confirmatory latent class models for a variety of item response types that are encountered in the clinical field. One or two timepoints are supported. Latent regression estimation can be performed, allowing for comparisons of longitudinal latent class assignments (e.g., treatment success/failure) across observed groups (e.g., treatment arms in clinical trials). Fit statistics C2 (a limited-information goodness-of-fit statistic), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) are available as well. Methods are described in Iaconangelo (2026) <doi:10.5281/zenodo.22663151>.

Authors:Charlie Iaconangelo [aut, cre]

CLCM_0.1.1.tar.gz
CLCM_0.1.1.zip(r-4.7-any)CLCM_0.1.1.zip(r-4.6-any)CLCM_0.1.1.zip(r-4.5-any)
CLCM_0.1.1.tgz(r-4.6-any)CLCM_0.1.1.tgz(r-4.5-any)
CLCM_0.1.1.tar.gz(r-4.7-any)CLCM_0.1.1.tar.gz(r-4.6-any)
CLCM_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CLCM/json (API)

# Install 'CLCM' in R:
install.packages('CLCM', repos = c('https://cjangelo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cjangelo/clcm/issues

Pkgdown/docs site:https://cjangelo.github.io

On CRAN:

Conda:

4.85 score 8 exports 3 dependencies

Last updated from:b6a7bf324e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK138
source / vignettesOK260
linux-releaseOK132
macos-releaseOK139
macos-oldrelOK176
windows-develOK74
windows-releaseOK83
windows-oldrelOK75
wasm-releaseOK114

Exports:aic_bic_clcmC2_clcmclcmclcm_2stagecompute_lpriorsimulate_clcmstart_with_zerotransition_matrix_clcm

Dependencies:latticeMatrixnumDeriv

C2 Fit Statistics
Vignette 2: C2 Fit Statistic | Specify Details of Data Generation | Simulate Item Responses | Estimate CLCM | Model Fit | Plots | Plot Model Implied Probabilities vs Observed Probabilities
Rendered fromVig_2.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2021-03-14

CLCM with Greater than 2 Latent Classes
Specify Details of Data Generation | The Q-matrix, alpha, eta and Condensation Rules - The Nitty Gritty | Latent Class Proportions | Simulate Item Responses | Estimate CLCM | Model Fit | Absolute Fit Statistic | Relative Fit Statistics | Classification Accuracy
Rendered fromVig_3.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2021-03-14

Introduction and Basic Examples
Overview of Vignettes | Introduction to CLCM R package | Specify Details of Data Generation | Simulate Item Responses | Estimate CLCM | Model Fit | Classification Accuracy | Plots
Rendered fromVig_1.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2021-03-14

Longitudinal CLCM & Transition Matrix
Specify Details of Data Generation | Specify transition matrix | Simulate Item Responses | Estimate CLCM | Transition Matrix | Classification Accuracy | Estimate CLCM with Constraint | Model Fit Comparison | Transition Matrices
Rendered fromVig_5.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2021-03-14

Longitudinal CLCM with Latent Regression
Specify Details of Data Generation | Subject Posterior Distributions via Latent Regression | Simulate Item Responses | Estimate CLCM | Transition Matrix | Classification Accuracy
Rendered fromVig_6.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2021-03-14

Longitudinal CLCM with Multinomial Latent Regression
Rendered fromVig_7.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2021-03-14

Multinomial Latent Regression
Specify Details of Data Generation | Subject Posterior Distributions via Multinomial Regression | Simulate Item Responses | Estimate CLCM | Results
Rendered fromVig_4.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2021-03-14