Title: Multi-Parent Population QTL Analysis
Authors@R: c(person("Vincent", "Garin", role = c("aut", "cre"),
person("Valentin", "Wimmer", role = c("aut")),
person("Dietrich", "Borchardt", role = c("ctb", "dtc")),
person("Fred", "van Eeuwijk", role = c("ctb", "ths")),
person("Marcos", "Malosetti", role = c("ctb", "ths")))
Description: Analysis of experimental multi-parent populations to detect
regions of the genome (called quantitative trait loci, QTLs)
influencing phenotypic traits. The population must be composed of crosses
between a set of at least three parents (e.g. factorial design,
'diallel', or nested association mapping). The functions cover data
processing, QTL detection, and results visualization. The implemented
methodology is described by Garin, Wimmer, Mezmouk, Malosetti and
Imports: ggplot2, graphics, grDevices, methods, parallel, qtl, stats,

Packaged: 2019-05-31 14:15:30 UTC; gebruiker

Packaged: 2020-02-10 03:23:46 UTC; gebruiker

Packaged: 2021-05-15 12:01:51 UTC; gebruiker
Author: Vincent Garin [aut, cre] (<https://orcid.org/0000-0002-5571-1841>),

Title: Statistics for analysing multiple simultaneous point processes
Author: Patrick Rubin-Delanchy <patrick.rubin-delanchy@bristol.ac.uk> and Nicholas A Heard <n.heard@imperial.ac.uk>
Description: A procedure to test for dependence between point processes on the real line, e.g. causal dependence, correlation, inhibition or anti-correlation. The package also provides a number of utilities for plotting simultaneous point processes, and combining p-values.

Description: Package for fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards.
Packaged: 2016-08-11 13:03:56 UTC; Kevin Burke

Packaged: 2016-09-21 09:59:10 UTC; Kevin Burke

Packaged: 2016-10-12 08:33:21 UTC; Kevin Burke

Packaged: 2019-12-12 11:51:10 UTC; Kevin Burke

Description: Fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards. See Burke & MacKenzie (2016) <doi:10.1111/biom.12625> and Burke et al (2020) <doi:10.1111/rssc.12398>.
Packaged: 2022-01-07 17:26:07 UTC; Kevin Burke