palettes appropriate for bivariate mapping and scale functions for 'ggplot2'
calls that adds those palettes to maps. Tools for creating bivariate legends
Imports: classInt, ggplot2, stats, utils
Suggests: covr, cowplot, knitr, rmarkdown, sf, testthat

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

Title: Estimating Cell Type Composition from Methylation Sequencing
Description: 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.
Suggests: dplyr, knitr, rmarkdown, tidyr, sirt, ggplot2

Title: Tools to find bad commits with git bisect
Description: Tools to find bad commits with git bisect

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

Title: Bayesian Isotonic Regression with Bernstein Polynomials
Author: S. McKay Curtis <smcurtis@stat.washington.edu>
Maintainer: S. McKay Curtis <smcurtis@stat.washington.edu>
Depends: R (>= 2.7.0), bootstrap, monreg, R2WinBUGS, coda
Description: Bayesian monotonic regression function

Author: S. McKay Curtis <s.mckay.curtis@gmail.com>
Maintainer: S. McKay Curtis <s.mckay.curtis@gmail.com>

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

Title: Approximate Bayesian Inference via Sparse Grid Quadrature
Evaluation (BISQuE) for Hierarchical Models
Description: Implementation of the 'bisque' strategy for approximate Bayesian posterior inference.  See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details.  'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models.  The resulting approximations are computationally efficient for many hierarchical Bayesian models.  The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.
Imports: mvQuad, Rcpp, doRNG, foreach, itertools
LinkingTo: Rcpp (>= 0.12.4), RcppArmadillo, RcppEigen (>= 0.3.3.3.1)
SystemRequirements: A system with a recent-enough C++11 compiler (such