Package: BTSPAS 2024.11.1

Carl J Schwarz

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>.

Authors:Carl J Schwarz [aut, cre], Simon J Bonner [aut]

BTSPAS_2024.11.1.tar.gz
BTSPAS_2024.11.1.tar.gz(r-4.7-any)BTSPAS_2024.11.1.tar.gz(r-4.6-any)
BTSPAS_2024.11.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BTSPAS/json (API)
NEWS

# Install 'BTSPAS' in R:
install.packages('BTSPAS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cschwarz-stat-sfu-ca/btspas/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

4.23 score 2 stars 28 scripts 723 downloads 13 exports 42 dependencies

Last updated from:81233b0b34. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK145
source / vignettesOK169
linux-release-x86_64OK160
wasm-releaseOK131

Exports:expitlogitRunTimeSimplePetersenTestIfPoolTimeStratPetersenDiagError_fitTimeStratPetersenDiagErrorWHChinook_fitTimeStratPetersenDiagErrorWHChinook2_fitTimeStratPetersenDiagErrorWHSteel_fitTimeStratPetersenNonDiagError_fitTimeStratPetersenNonDiagErrorNP_fitTimeStratPetersenNonDiagErrorNPMarkAvail_fitTimeToTargetRunSize

Dependencies:abindactuarbase64encbootclicodacpp11data.tableexpintfarverggforceggplot2gluegridExtragtableisobandjsonlitelabelinglatticelifecyclemagrittrMASSplyrpolyclipR2jagsR2WinBUGSR6RColorBrewerRcppreshape2rjagsrlangS7scalesstringistringrsystemfontstidyselecttweenrvctrsviridisLitewithr

01 Diagonal model

Rendered froma-Diagonal-model.html.asisusingR.rsp::asison Jun 01 2026.

Last update: 2019-12-04
Started: 2019-12-04

02 Diagonal model with multiple ages

Rendered fromb-Diagonal-model-with-multiple-ages.html.asisusingR.rsp::asison Jun 01 2026.

Last update: 2019-12-04
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03 Non-diagonal model

Rendered fromc-Non-diagonal-model.html.asisusingR.rsp::asison Jun 01 2026.

Last update: 2019-12-04
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04 Non-diagonal with fall-back model

Rendered fromd-Non-diagonal-with-fall-back-model.html.asisusingR.rsp::asison Jun 01 2026.

Last update: 2019-12-04
Started: 2019-12-04

05 Bias from incomplete sampling

Rendered frome-Bias-from-incomplete-sampling.html.asisusingR.rsp::asison Jun 01 2026.

Last update: 2019-12-04
Started: 2019-12-04

06 Interpolating run earlier and later

Rendered fromf-Interpolating-run-earlier-and-later.html.asisusingR.rsp::asison Jun 01 2026.

Last update: 2020-09-05
Started: 2020-09-05