Packaged: 2022-09-06 20:38:58 UTC; marcgirondot

Packaged: 2022-10-19 14:48:04 UTC; marcgirondot

Packaged: 2022-11-09 20:49:25 UTC; marcgirondot

Title: Heritability of Gene Expression for Next-Generation Sequencing
Maintainer: W. Jenny Shi <wjennyshi@gmail.com>
Description: Statistical framework to analyze heritability of gene expression
based on next-generation sequencing data and simulating sequencing reads.
Variance partition coefficients (VPC) are computed using linear mixed effects
and generalized linear mixed effects models. Compound Poisson and negative
Imports: cplm, DESeq2, lme4, pbapply, tweedie, MASS,
Packaged: 2017-01-04 07:44:16 UTC; wenjennyshi

binomial models are included. Reference: Rudra, Pratyaydipta, et al. "Model based heritability scores for high-throughput sequencing data." BMC bioinformatics 18.1 (2017): 143.
Packaged: 2019-02-11 16:59:28 UTC; wenjennyshi

Packaged: 2021-07-12 04:27:30 UTC; wenjennyshi

Title: Test of No Main and/or Interaction Effects in Functional Data
Author: Haiyan Wang, Mickael Akritas, James Higgins, Dale Blasi
Description: Distribution free heteroscedastic tests for functional data.
The following tests are included in this package: test of no main treatment or contrast
effect and no simple treatment effect given in
no main time effect, and no interaction effect based on
original observations given in Wang and Akritas (2010a)
and tests based on ranks given in Wang and Akritas (2010b)

Title: Gaussian Graphical Model-Based Heterogeneity Analysis
Authors@R: c(person("Mingyang", "Ren", role=c("aut", "cre"),
Description: The goal of this package is to user-friendly realizing Gaussian
Recently, several Gaussian graphical model-based heterogeneity
analysis techniques have been developed. A common methodological limitation
is that the number of subgroups is assumed to be known a priori, which
is not realistic. In a very recent study (Ren et al., 2021), a novel approach
based on the penalized fusion technique is developed to fully
data-dependently determine the number and structure of subgroups in
Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing
the Gaussian graphical model technique in more practical settings. Beyond
Ren et al. (2021), more estimations and functions are added, so
that the package is self-contained and more comprehensive and can
provide "more direct" insights to practitioners (with the
Ren, M., Zhang S., Zhang Q. and Ma S. (2021). Gaussian Graphical
Model-based Heterogeneity Analysis via Penalized Fusion.
Packaged: 2021-02-07 00:17:54 UTC; Mingyang Ren