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:	2021.11.2
Imports:	actuar, coda, data.table, ggplot2, ggforce, graphics, grDevices, gridExtra, plyr, reshape2, R2jags, scales, splines, stats, utils
Suggests:	R.rsp
Published:	2021-10-25
Author:	Carl J Schwarz and Simon J Bonner
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.pdf
Vignettes:	01 Diagonal model 02 Diagonal model with multiple ages 03 Non-diagonal model 04 Non-diagonal with fall-back model 05 Bias from incomplete sampling 06 Interpolating run earlier and later

Downloads:

Package source:	BTSPAS_2021.11.2.tar.gz
Windows binaries:	r-devel: BTSPAS_2021.11.2.zip, r-release: BTSPAS_2021.11.2.zip, r-oldrel: BTSPAS_2021.11.2.zip
macOS binaries:	r-release (arm64): BTSPAS_2021.11.2.tgz, r-oldrel (arm64): BTSPAS_2021.11.2.tgz, r-release (x86_64): BTSPAS_2021.11.2.tgz, r-oldrel (x86_64): BTSPAS_2021.11.2.tgz
Old sources:	BTSPAS archive

Linking:

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