Package: BayesFR 1.0.1

Benjamin Rosenbaum
BayesFR: Fitting Functional Responses in 1- and 2-Prey Systems
Easy application of Bayesian inference for functional responses via 'brms'. This package allows to fit various FR models for single- and multi-prey experiments by providing nonlinear prediction functions for 'brms'. It uses dynamical prediction models to correct for prey depletion. The 'brms' framework facilitates statistical modeling and enables users to conveniently incorporate covariates such as temperature gradients, experimental treatment variables, or random effects that account for grouping in experimental units. Default 'brms' functions make it easy to perform model checking, model comparison and hypothesis testing. Potential statistical issues with data from feeding trials, such as overdispersion, can be resolved by effortlessly switching between likelihood functions. This package, together with its tutorials, should provide students and researchers with a comprehensive and integrated statistical framework for easily testing their hypotheses on trophic interactions. References: Rosenbaum and Rall (2018) <doi:10.1111/2041-210X.13039>; Rosenbaum et al. (2024) <doi:10.1111/2041-210X.14372>.
Authors:
BayesFR_1.0.1.tar.gz
BayesFR_1.0.1.tar.gz(r-4.7-any)BayesFR_1.0.1.tar.gz(r-4.6-any)
BayesFR_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BayesFR/json (API)
| # Install 'BayesFR' in R: |
| install.packages('BayesFR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/benjamin-rosenbaum/bayesfr/issues
- df_Archer_et_al_2019_JAE - Example dataset for prey mortality
- df_Colton_1987_1_ECOLOGY - Example dataset for multi-species FR with 2 prey
- df_Cuthbert_et_al_2020_ECOL_EVOL - Example dataset for categorical predictors
- df_Davidson_et_al_2021_FUN_ECOL - Example dataset for continuous predictors
- df_Hossie_and_Murray_2010_OECOLOGIA - Feeding experiments without prey replacement
- df_Michalko_and_Pekar_2017_AM_NAT - Feeding experiments with prey replacement
- df_Papanikolaou_et_al_2021_ECOL_EVOL - Example dataset for testing predator interference models
- df_Schroeder_et_al_2016_OEC - Example dataset for random effects
- df_Sentis_et_al_2017_GLOBAL_CHANGE_BIOLOGY - Example dataset for testing type 2 vs. type 3
Last updated from:40d59b943c. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 217 | ||
| source / vignettes | OK | 227 | ||
| linux-release-x86_64 | OK | 199 | ||
| wasm-release | OK | 185 |
Exports:convert_2sp_to_longMS_Type2H_dyn_codeMS_Type3H_dyn_codeMS_TypeY_dyn_codeMS_TypeZ_dyn_codeType1_dyn_codeType1_fix_codeType2BD_dyn_codeType2CM_dyn_codeType2H_dyn_codeType2H_fix_codeType2HV_dyn_codeType3GenH_dyn_codeType3GenH_fix_codeType3GenH_mort_dyn_codeType3H_dyn_codeType3H_fix_codeTypeGenBD_dyn_codeTypeGenCM_dyn_codeTypeGenHV_dyn_code
Dependencies:abindbackportsbayesplotBHbridgesamplingbrmsBrobdingnagcallrcheckmateclicodacodetoolscpp11descdigestdistributionaldplyrfarverfuturefuture.applygenericsggplot2ggridgesglobalsgluegridExtragtableinlineisobandlabelinglatticelifecyclelistenvloomagrittrMatrixmatrixStatsmgcvmvtnormnleqslvnlmenumDerivotelparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsS7scalesStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr