person("Xin", "Guo", , "phoeguo@gmail.com", c("aut", "cre"))
Suggests: shiny (>= 1.0.0), knitr, rmarkdown

Title: Interactively Visualize Genomic Mutation Data using a
person("Xin", "Guo", email = "g3viz.group@gmail.com", role = c("aut", "cre"))
Description: R interface for 'g3-lollipop' JavaScript library.
Visualize genomic mutation data using an interactive lollipop diagram in Rstudio or your browser.

Title: Interactively Visualize Genetic Mutation Data using a
Visualize genetic mutation data using an interactive lollipop diagram in RStudio or your browser.
Suggests: shiny (>= 1.0.0), knitr, rmarkdown, kableExtra

person("Xin", "Guo", email = "g3viz.group@gmail.com", role = c("aut","cre")),
person("Rener", "Zhang", email = "g3viz.group@gmail.com", role = c("ctb"))
Description: Interface for 'g3-lollipop' JavaScript library.
Visualize genetic mutation data using an interactive lollipop diagram in Studio or your browser.
Imports: jsonlite, stringr, cBioPortalData, htmlwidgets

Title: Graph-based two-sample tests for categorical data
Description: These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.

Title: Graph-Based Two-Sample Tests for Categorical Data

Imports: maptools, rgeos, rgdal, igraph, methods
Author: Frederico Mestre, Fernando Ascensao, A. Marcia Barbosa
Description: Provides a set of tools to help the de-fragmentation process. It works by prioritizing the different sections of linear infrastructures (e.g. roads, power-lines) to increase the available amount of a given resource.

Packaged: 2020-09-02 19:38:09 UTC; Frederico

Title: Generalized Iterative Proportional Fitting for Relational Models
Description: Maximum likelihood estimation under relational models,

Description: Maximum likelihood estimation under relational models, with or without the overall effect.

Title: A Generalized Joint Location and Scale Framework for Association
Description: An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).
Imports: methods, nlme, quantreg, MCMCpack, MASS, plyr, ggplot2,