Estimation of latent class models with individual covariates for capture-recapture data. See Bartolucci, F. and Forcina, A. (2022), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80(2), ujae017.

Providing a method for Local Discrimination via Latent Class Models. The approach is described in <https://www.r-project.org/conferences/useR-2009/abstracts/pdf/Bucker.pdf>.

This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.

A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.

Bayesian population size estimation using non parametric latent-class models.

Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

The goal of 'LCMSQA' is to make it easy to check the quality of
             liquid chromatograph/mass spectrometry (LC/MS) experiments using a
             'shiny' application. This package provides interactive data
             visualizations for quality control (QC) samples, including total
             ion current chromatogram (TIC), base peak chromatogram (BPC), mass
             spectrum, extracted ion chromatogram (XIC), and feature detection
             results from internal standards or known metabolites.