classInt, dplyr, ggplot2, glue, rlang, sf, stats, tidyr

c(
person("Christopher", "Prener", , "chris.prener@gmail.com", c("aut", "cre"),
comment = c(ORCID = "0000-0002-4310-9888")),
person("Timo", "Grossenbacher", , role = "aut"),
person("Angelo", "Zehr", , role = "aut"),
person("Joshua", "Stevens", , role = "ctb"))
Provides a 'ggplot2' centric approach to bivariate mapping. This is a
technique that maps two quantities simultaneously rather than the single value
that most thematic maps display. The package provides a suite of tools
for calculating breaks using multiple different approaches, a selection of
palettes appropriate for bivariate mapping and scale functions for 'ggplot2'
calls that adds those palettes to maps. Tools for creating bivariate legends
are also included.
covr, cowplot, knitr, rmarkdown, sf, testthat

Auxiliary functions for phenological data analysis
Provides some easy-to-use functions for spatial analyses
of (plant-) phenological data sets and satellite observations
of vegetation.

Estimating Cell Type Composition from Methylation Sequencing
Data
An implementation of Bisect, a method for inferring cell type composition of samples based on methylation sequencing data (Whole Genome Bisulfite Sequencing and Reduced Representation Sequencing). The method is specifically tailored for sequencing data, and therefore works better than methods developed for methylation arrays. It contains a supervised mode that requires a reference (the methylation probabilities in the pure cell types), and a semi-supervised mode, that requires cell counts for a subset of the samples, but does not require a reference.
dplyr, knitr, rmarkdown, tidyr, sirt, ggplot2

Tools to find bad commits with git bisect

Tools to find bad commits with git bisect. See
https://github.com/wch/bisectr for examples and test script
templates.

Bayesian Isotonic Regression with Bernstein Polynomials
R (>= 2.7.0), bootstrap, monreg, R2WinBUGS, coda

R (>= 2.7.0), bootstrap, monreg, R2WinBUGS, coda (>= 0.17.1)
Provides functions for fitting Bayesian monotonic regression models to data.