testthat, knitr, rmarkdown, R.rsp, MASS, plotly (>= 4.7.1)

Runs a linear-like regression with in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.

A Package to Visualize Linear Models Features and Play with Them
Francesco Pauli (see file LICENSEMEDIA for credits on sounds and images)
Contains three shiny applications.
Two are meant to explore linear model inference feature through simulation.
The third is a game to learn interpreting diagnostic plots.

Contains a suite of shiny applications
meant to explore linear model inference feature through simulation
and games.
shiny, shinyjs, lmtest, mgcv, methods, MASS, scatterplot3d,
rgl, car

Logistic-Normal Model for Clustered Binary (Binomial) Data
S original by Patrick Heagerty, ported by Bryan Comstock
Maximum likelihood estimation for clustered binary data

Marginally specified logisti normal models for longitudinal binary data

Long Non-Coding RNA Differential Expression Analysis
We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>.

Perform Logistic Normal Multinomial Clustering for Microbiome
Compositional Data
An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.
mclust, foreach, MASS, stringr, gtools, pgmm, utils
Wangshu Tu [aut, cre],
Sanjeena Dang [aut],
Yuan Fang [aut]