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BTSPAS: Bayesian Time-Stratified Population Analysis

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 the daily run size. Theory described in Bonner and Schwarz (2011) <doi:10.1111/j.1541-0420.2011.01599.x>.

Version:	2024.11.1
Imports:	actuar,coda,data.table,ggplot2,ggforce, graphics, grDevices,gridExtra,plyr,reshape2,R2jags,scales, splines, stats, utils
Suggests:	R.rsp
Published:	2024-10-23
DOI:	10.32614/CRAN.package.BTSPAS
Author:	Carl J Schwarz [aut, cre], Simon J Bonner [aut]
Maintainer:	Carl J Schwarz <cschwarz.stat.sfu.ca at gmail.com>
License:	GPL-2 \|GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/cschwarz-stat-sfu-ca/BTSPAS
NeedsCompilation:	no
SystemRequirements:	JAGS
Citation:	BTSPAS citation info
Materials:	README,NEWS
CRAN checks:	BTSPAS results

Documentation:

Reference manual:	BTSPAS.html ,BTSPAS.pdf
Vignettes:	01 Diagonal model (source) 02 Diagonal model with multiple ages (source) 03 Non-diagonal model (source) 04 Non-diagonal with fall-back model (source) 05 Bias from incomplete sampling (source) 06 Interpolating run earlier and later (source)

Downloads:

Package source:	BTSPAS_2024.11.1.tar.gz
Windows binaries:	r-devel:BTSPAS_2024.11.1.zip, r-release:BTSPAS_2024.11.1.zip, r-oldrel:BTSPAS_2024.11.1.zip
macOS binaries:	r-release (arm64):BTSPAS_2024.11.1.tgz, r-oldrel (arm64):BTSPAS_2024.11.1.tgz, r-release (x86_64):BTSPAS_2024.11.1.tgz, r-oldrel (x86_64):BTSPAS_2024.11.1.tgz
Old sources:	BTSPAS archive

Reverse dependencies:

Reverse imports:

Petersen

Linking:

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