GH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).
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Updated
Jun 3, 2024 - HTML
GH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).
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.
An R Package for Multiple-Group Latent Class Analysis
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.
👩🍳 🥧 Bayesian Analysis Kit for Etiology Research via Nested Partially Latent Class Models
Code for evaluating the reproducibility of the POC-CCA diagnostic for Schistosomiasis across two settings in Uganda
Investigating the efficacy of diagnostic kits used for parasitic disease surveillance in the Philippines.
C++ Implementation of poLCA (R package)
Supplementary materials for the manuscript "Latent-class trajectory modeling with a heterogeneous mean-variance relation" by N. G. P. Den Teuling, F. Ungolo, S.C. Pauws, and E.R. van den Heuvel
Code for comparing three different diagnostics in the detection of two ruminant flukes in the South of Italy
Survival Analysis with Neural Networks
This walkthrough is presented by the IMMERSE team and will go through some common tasks carried out in R.
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).
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.
Demonstrate the speed of running an LCA analysis using MplusAutomation
This is a statistical analysis research project on Analyzing Client Behavior in The Connection, sponsored by the Connection Inc. and Wesleyan Quantitative Analysis Center.
Python implementation of Multinomial Logit Model
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