Title: Training and Analysing Asynchronous Boolean Models
Maintainer: Chee Yee Lim <cyl49@cam.ac.uk>
Description: Tools for inferring asynchronous Boolean
models from single-cell expression data.
Imports: parallel, Rcpp (>= 0.11.4), foreach (>= 1.4.1), doParallel (>=
Suggests: bnlearn (>= 3.8.1), knitr, rmarkdown

Title: Bayesian Time-Strat. Population Analysis
Author: Carl J Schwarz <cschwarz@stat.sfu.ca> and Simon J Bonner
Maintainer: Simon J Bonner <s.bonner@stat.ubc.ca>
Depends: R2WinBUGS, coda, splines, actuar, BRugs
Description: BTSPAS provides advanced Bayesian methods to estimate
abundance and run-timing from temporally-stratified Petersen
mark-recapture experiments. Methods include hierarchical
modelling of the capture probabilities and spline smoothing of
SystemRequirements: Currently only MS-Windows is supported (neither
R2WinBUGS or BRugs support other platforms/OS types at this

Maintainer: Carl J Schwarz <cschwarz@stat.sfu.ca>

Depends: coda, splines, actuar, BRugs, R2WinBUGS

Depends: coda, splines, actuar, BRugs, R2OpenBUGS

Author: Carl J Schwarz <cschwarz@stat.sfu.ca> and
Imports: coda, splines, actuar, rjags, R2OpenBUGS, ggplot2, plyr
abundance and run-timing from temporally-stratified
Petersen mark-recapture experiments. Methods include
hierarchical modelling of the capture probabilities
and spline smoothing of the daily run size. This version
uses JAGS to sample from the posterior distribution.

Title: Bayesian Time-Stratified Population Analysis
Author: Carl J Schwarz <cschwarz.stat.sfu.ca@gmail.com> and
Maintainer: Carl J Schwarz <cschwarz.stat.sfu.ca@gmail.com>
Imports: actuar, coda, data.table, ggplot2, ggforce, graphics,
grDevices, gridExtra, plyr, reshape2, R2jags, splines, stats,
Description: Provides advanced Bayesian methods to estimate
and spline smoothing of the daily run size.

grDevices, gridExtra, plyr, reshape2, R2jags, scales, splines,

and spline smoothing of the daily run size. Theory

person(c("Jack", "Joseph"), "Dongarra", role = ("ctb")),
person("Cleve", "Moler", role = ("ctb")),
person(c("Gilbert", "Wright"), "Stewart", role = ("ctb")),
person(c("Richard", "H."), "Byrd", role = ("ctb")),
person("Jorge", "Nocedal", role = ("ctb")),
person(c("Jose", "Luis"), "Morales", role = ("ctb")),
person("Peihuang", "Lu-Chen", role = ("ctb")),
person("John", "Burkardt", role = ("ctb")),
person("Alan", "Miller", role = ("ctb")),
person("B.E.", "Schneider", role = ("ctb")),
person(c("Alfred", "H."), "Morris", role = ("ctb")),
person(c("Robert", "W.M."), "Wedderburn", role = ("ctb")),
person("Jason", "Blevins", role = ("ctb")),
person("Brian", "Wichman", role = ("ctb")),
person("David", "Hill", role = ("ctb")),
person("Hiroshi", "Takano", role = ("ctb")),
person("George", "Marsaglia", role = ("ctb")),
person("Jean-Michel", "Brankart", role = ("ctb")),
person("Steve", "Kifowit", role = ("ctb")),
person(c("Donald", "E."), "Knuth", role = ("ctb")),
person("Catherine", "Loader", role = ("ctb")))
Description: Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in 'FORTRAN', which translates into very fast algorithms. The main references are
Author: Taiane Schaedler Prass [aut, cre, com]
Guilherme Pumi [ctb, aut] (<https://orcid.org/0000-0002-6256-3170>),
Fábio Mariano Bayer [ctb] (<https://orcid.org/0000-0002-1464-0805>),
Maintainer: Taiane Schaedler Prass <taianeprass@gmail.com>