Title: Analysing and Plotting Genotype-Phenotype Maps
Description: Tools for studying genotype-phenotype maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al (2013) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Frontier in Genetics 4:216 <doi:10.3389/fgene.2013.00216>.

Arne B. Gjuvsland <arne.gjuvsland@umb.no> and Yunpeng Wang <yunpeng.wng@gmail.com>
Maintainer: Arne B. Gjuvsland <arne.gjuvsland@umb.no>
Depends: isotone, plyr, ggplot2, foreach, doMC
Description: This package contains tools for studying genotype-phenotype (GP) maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al. (2003) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E, Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Submitted to Frontiers in Genetics

Description: Provides an implementation of Gaussian process panel modeling (GPPM).
GPPM is described in Karch (2016; <DOI:10.18452/17641>) and Karch, Brandmaier & Voelkle (2018; <DOI:10.17605/OSF.IO/KVW5Y>).
Essentially, GPPM is Gaussian process based modeling of longitudinal panel data.
'gppm' also supports regular Gaussian process regression (with a focus on flexible model specification), and multi-task learning.
Authors@R: c(person(c("Julian", "D."), "Karch", email="j.d.karch@fsw.leidenuniv.nl", role=c("aut", "cre", "cph")))
ggthemes (>= 3.5.0), mvtnorm (>= 1.0-8), stats, methods
Suggests: testthat (>= 2.0.0), knitr (>= 1.20), rmarkdown (>= 1.10),
Maintainer: Julian D. Karch <j.d.karch@fsw.leidenuniv.nl>

Title: Gaussian Processes for Poisson-noised Data
Maintainer: Charles R. Hogg III <charles.hogg@nist.gov>
Description: Analyzes data using Gaussian processes.  Supported noise
models include Gaussian or Poisson; the latter is handled using
the Anscombe transform to turn it into Gaussian noise.
'geometry.R' 'gppois.R' 'layout.R' 'poisson.R' 'rgl.R'