Manipulate, assemble, export <http://www.gadm.org> maps. Create 'choropleth', 'isopleth', dots plot, proportional dots and more.
R (>= 3.5.0), sp, tidyverse, classInt, sf, rgdal
methods, RColorBrewer, maptools, stringr, raster, rosm,
lattice, jsonlite, gridExtra, rgeos, ggmap, ggspatial, ggplot2,
dplyr, prettymapr
knitr, rmarkdown, kableExtra, mapproj, testthat

Manipulate, assemble, export <http://www.gadm.org> maps. Create 'choropleth', 'isopleth', dots plot, proportional dots,
dot-density and more.
knitr, rmarkdown, kableExtra, mapproj, tidyr, testthat

Manipulate, assemble, export <https://gadm.org/> maps. Create 'choropleth', 'isopleth', dots plot, proportional dots,
dot-density and more.

Global Adaptive Generative Adjustment Algorithm for Generalized
Linear Models
c(
person("Bin", "Wang", role=c("aut", "cre"), email="eatingbeen@hotmail.com"),
person("Xiaofei", "Wang", role=c("ctb")),
person("Jianhua", "Guo", role=c("ths"))
)
Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>.
Bin Wang [aut, cre],
Xiaofei Wang [ctb],
Jianhua Guo [ths]

Graphical Extension with Accuracy in Parameter Estimation
(GAIPE)
GAIPE implements graphical representation of accuracy in
parameter estimation (AIPE) on RMSEA for sample size planning
in structural equation modeling. Sample sizes suggested by
RMSEA with AIPE method and power analysis approach can also be
obtained separately using the provided functions.

Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA.

Genomic Annotation in Livestock for Positional Candidate LOci
c(person(given = "Pablo",
family = "Fonseca",
role = c("aut", "cre"),
email = "pfonseca@uoguelph.ca"), person("Aroa", "Suarez-Vega", email = "asuarezv@uoguelph.ca", role = "aut"),person("Gabriele", "Marras", email = "gmarras@semex.com", role = "aut"),person("Angela", "Cánovas", email = "acanovas@uoguelph.ca", role = "aut"))
The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.
circlize, DT, data.table, doParallel, dplyr, dynamicTreeCut,
ggplot2, graphics, grDevices, foreach, lattice , parallel,
RColorBrewer, rtracklayer, stats, stringr, unbalhaar, utils
Pablo Fonseca [aut, cre],
Aroa Suarez-Vega [aut],
Gabriele Marras [aut],
Angela Cánovas [aut]