A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

An R Package for Multiple-Group Latent Class Analysis

👩‍🍳 🥧 Bayesian Analysis Kit for Etiology Research via Nested Partially Latent Class Models

This module provides Latent Class Analysis, Laten Profile Analysis, Rasch model, Linear Logistic Test Model, and Rasch mixture model including model information,fit statistics,and bootstrap fit based on JMLE. Furthermore, linear and equipercentile equating can be performed within module.

This package fits a latent class CTMC model to cluster longitudinal multistate data

This R package simulates data from a latent class CTMC model

This R tutorial automates the 3-step ML auxiliary variable procedure using the MplusAutomation package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters. To learn more about auxiliary variable integration methods and why multi-step methods are necessary for producing un-biased estimates see Asparouhov & Muthén (2014).

C++ Implementation of poLCA (R package)

Survival Analysis with Neural Networks

Python implementation of Multinomial Logit Model

This walkthrough is presented by the IMMERSE team and will go through some common tasks carried out in R.

Investigating the efficacy of diagnostic kits used for parasitic disease surveillance in the Philippines.

This `R` tutorial automates the BCH two-step axiliary variable procedure (Bolk, Croon, Hagenaars, 2004) using the `MplusAutomation` package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters.

GH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).

Demonstrate the speed of running an LCA analysis using MplusAutomation

Latent Class Analysis on German Credit Data Set to find the latent variables affecting the credit outcomes and behaviour

Code for evaluating the reproducibility of the POC-CCA diagnostic for Schistosomiasis across two settings in Uganda

⏪ R package for: Reconstructing Etiology with Binary Decomposition