Package: ubms 1.2.9

Ken Kellner

ubms: Bayesian Models for Data from Unmarked Animals using 'Stan'

Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Authors:Ken Kellner [cre, aut]

ubms_1.2.9.tar.gz
ubms_1.2.9.zip(r-4.7)ubms_1.2.9.zip(r-4.6)ubms_1.2.9.zip(r-4.5)
ubms_1.2.9.tgz(r-4.6-arm64)ubms_1.2.9.tgz(r-4.6-x86_64)ubms_1.2.9.tgz(r-4.5-x86_64)ubms_1.2.9.tgz(r-4.5-arm64)
ubms_1.2.9.tar.gz(r-4.7-arm64)ubms_1.2.9.tar.gz(r-4.7-x86_64)ubms_1.2.9.tar.gz(r-4.6-arm64)ubms_1.2.9.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ubms/json (API)

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

Bug tracker:https://github.com/ecoverser/ubms/issues

Pkgdown/docs site:https://ecoverser.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

distance-samplinghierarchical-modelsn-mixture-modeloccupancystanopenblascpp

8.37 score 38 stars 154 scripts 774 downloads 41 exports 59 dependencies

Last updated from:c75d0df545. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK465
linux-devel-x86_64OK445
source / vignettesOK571
linux-release-arm64OK507
linux-release-x86_64OK476
macos-release-arm64OK265
macos-release-x86_64OK923
macos-oldrel-arm64OK307
macos-oldrel-x86_64OK596
windows-develOK784
windows-releaseOK639
windows-oldrelOK1137
wasm-releaseFAIL199

Exports:cauchycoefextractextract_log_likfitListgammaget_elapsed_timeget_stancodegetYgofkfoldlaplacelogisticloomodSelnormalnsamplesplot_effectsplot_marginalplot_posteriorsplot_residualsplot_spatialposterior_linpredposterior_predictpredictprojectedranefRSRstan_colextstan_distsampstan_multinomPoisstan_occustan_occuRNstan_occuTTDstan_pcountstudent_tsummarytraceplotturnoveruniformwaic

Dependencies:abindbackportsBHcallrcheckmateclicpp11descdistributionalfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsnumDerivotelpbapplypillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreformulasrlangRSpectrarstanrstantoolsS7scalesStanHeaderstensorAtibbleTMBunmarkedutf8vctrsviridisLitewithr

Overview of ubms
Introduction | What is ubms? | Why should you use it? | What are the disadvantages? | Example: Fitting a single-season occupancy model | Set up the data | Fit a model | Fit the model in unmarked | Fit the model in ubms | Compare results | Understanding the ubms output summary | Extracting individual parameters | Compare candidate models | Fit the models | Compare the models | Diagnostics and model fit | MCMC diagnostics | Model fit | Model inference | Marginal covariate effects | Predict parameter values | Simulate latent occupancy states | References
Rendered fromubms.Rmdusingknitr::rmarkdown

Last update: 2025-09-29
Started: 2020-09-17

Spatial Models in ubms
Introduction | Example | Format the input data | Choose RSR options | Fit the model | Examine results | Plotting | Model selection | Compare with stocc | References
Rendered fromspatial-models.Rmdusingknitr::rmarkdown

Last update: 2024-09-15
Started: 2021-06-23

Comparing models fit in ubms and JAGS
Introduction | Simulating the data | Fitting the model in ubms | Fitting the model in JAGS | Compare fixed effect estimates | Compare random effect estimates
Rendered fromJAGS-comparison.Rmdusingknitr::rmarkdown

Last update: 2022-11-10
Started: 2020-10-30

Random effects in ubms
Introduction | "Stacked" Models | Fitting a stacked model with ubms | Read in the input data | Convert the input data to stacked format | Fit the Stacked Model | Accessing the random intercepts | References
Rendered fromrandom-effects.Rmdusingknitr::rmarkdown

Last update: 2021-09-25
Started: 2020-10-06

Readme and manuals

Help Manual

Help pageTopics
Extract a Submodel from a ubmsFit Model[,ubmsFit,character,missing,missing-method
Extract a ubmsSubmodel From a ubmsSubmodelList Object[,ubmsSubmodelList,character,missing,missing-method
Extract Coefficient Values From a ubmsFit Modelcoef,ubmsFit-method
Extract Pointwise Log-likelihood From Modelextract_log_lik extract_log_lik,ubmsFit-method extract_log_lik,ubmsFitDistsamp-method
Extract Samples From a ubmsFit Modelextract,ubmsFit-method
Create a List of ubmsFit ModelsfitList fitList,list-method fitList,ubmsFit-method
Extract Fitted Valuesfitted,ubmsFit-method
Get Model Runtimeget_elapsed_time,ubmsFit-method
Get Stan Code From Modelget_stancode,ubmsFit-method
Extract y, the Response Variable, From a ubmsFit ModelgetY,ubmsFit-method
Check model goodness-of-fitgof gof,ubmsFitAbun-method gof,ubmsFitOccu-method plot,ubmsGOF,ANY-method
K-fold Cross-validation of a ubmsFit Modelkfold,ubmsFit-method
Leave-one-out Cross Validationloo,ubmsFit-method
Model Selection For a List of ubmsFit ModelsmodSel,ubmsFitList-method
Get Parameter Names From a ubmsFit Modelnames,ubmsFit-method
Get Names of Models in a ubmsFitListnames,ubmsFitList-method
Get number of Posterior Samples Stored in a ubmsFit Modelnsamples,ubmsFit-method
Plot Marginal Effects of Covariatesplot_effects plot_effects,ubmsFit-method plot_marginal plot_marginal,ubmsFit-method
Plot Posterior Distributionsplot_posteriors plot_posteriors,ubmsFit-method
Plot Model Residualsplot_residuals plot_residuals,ubmsFit-method
Plot A Map of the State Parameter Based on a Spatial ubms Modelplot_spatial
Plot Residuals For All Submodels in a ubmsFit Modelplot,ubmsFit,ANY-method
Posterior Distribution of the Linear Predictorposterior_linpred posterior_linpred,ubmsFit-method
Draw from the posterior predictive distributionposterior_predict posterior_predict,ubmsFit-method
Predict parameter values from a fitted modelpredict predict,ubmsFit-method
Prior distributionscauchy gamma laplace logistic normal priors student_t uniform
Projected Occupancy Trajectoriesprojected projected,ubmsFitColext-method
Extract Random Effectsranef ranef,ubmsFit-method
Extract Model Residualsresiduals,ubmsFit-method
Get Information for a Restricted Spatial Regression ModelRSR
Fit the MacKenzie et al. (2003) Dynamic Occupancy Modelstan_colext
Fit the Royle et al. (2004) Distance Sampling Modelstan_distsamp
Fit the Multinomial-Poisson Mixture Modelstan_multinomPois
Fit the MacKenzie et al. (2002) Occupancy Modelstan_occu
Fit the Occupancy Model of Royle and Nichols (2003)stan_occuRN
Fit Time-to-detection Occupancy Modelsstan_occuTTD
Fit the N-mixture model of Royle (2004)stan_pcount
Extract Summary Statistics from a ubmsFit Modelsummary,ubmsFit-method
Markov Chain Traceplotstraceplot,ubmsFit-method
Turnover Probabilityturnover turnover,ubmsFitColext-method
ubmsubms-package ubms
Extractors for ubmsFitList objects Extract parts of ubmsFitList objects.$,ubmsFitList-method ubmsFitList-extractors [,ubmsFitList,numeric,missing,missing-method [[,ubmsFitList,numeric,missing-method
Widely Applicable Information Criterion (WAIC)waic,ubmsFit-method