Suggests: testthat, knitr, rmarkdown, R.rsp, MASS

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

Description: 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.

Title: A Package to Visualize Linear Models Features and Play with Them
Author: Francesco Pauli (see file LICENSEMEDIA for credits on sounds and images)
Description: 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.
Imports: shiny, shinyjs, lmtest, mgcv, methods

Description: Contains a suite of shiny applications
meant to explore linear model inference feature through simulation
Imports: shiny, shinyjs, lmtest, mgcv, methods, MASS, scatterplot3d,

Title:  Logistic-Normal Model for Clustered Binary (Binomial) Data
Author: S original by Patrick Heagerty, ported by Bryan Comstock
Description: Maximum likelihood estimation for clustered binary data
Packaged: Mon Jan 28 10:49:58 2008; install

Title: Marginally specified logisti normal models for longitudinal binary data
Packaged: Tue Jan 22 12:17:56 2008; install

Title: Long Non-Coding RNA Differential Expression Analysis
Description: 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>.

Title: Perform Logistic Normal Multinomial Clustering for Microbiome
Description: 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>.
Imports: mclust, foreach, MASS, stringr, gtools, pgmm, utils
Suggests: knitr, rmarkdown, testthat, mvtnorm
Packaged: 2022-07-19 19:53:07 UTC; wangshutu