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
Latent Class Analysis(LCA), LCA for ordinal indicators, Latent class growth modeling, Laten Profile Analysis, Rasch model, Linear Logistic Test Model, Rasch mixture model, 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
Investigating the efficacy of diagnostic kits used for parasitic disease surveillance in the Philippines.
A Time-Dependent Structural Model Between Latent Classes and Competing Risks Outcomes
⏪ R package for: Reconstructing Etiology with Binary Decomposition
Code for evaluating the reproducibility of the POC-CCA diagnostic for Schistosomiasis across two settings in Uganda
SLTCA: Scalable and Robust Latent Trajectory Class Analysis Using Artificial Likelihood
Civic education is an important subject for every citizen in our modern society. It is important that every individual acknowledge the importance of civil rights and obligations. One of the most commented topics is how society faces and behaves towards the great diversity of individuals and cultures. Students are a great population to be studied…
The project uncovers distinct groups of voter profiles utilizing COVID-19 orientations in a representative sample of German voters. The distinct groups in the population differ in their beliefs towards the effectiveness of government measures, compliance with a possible curfew, and trust in various institutions.
Code for comparing three different diagnostics in the detection of two ruminant flukes in the South of Italy
Genetic alterations and expression of genes coding for FGF ligands and FGF reseptors in urothelial cancer
Clinical significance of sex hormone levels in testis carcinoma
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
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