Package: ubms
Version: 1.0.1
Date: 2021-01-15
Title: Bayesian Models for Data from Unmarked Animals using 'Stan'
Authors@R: person("Ken", "Kellner", email="contact@kenkellner.com", 
                  role=c("cre","aut"))
Depends: R (>= 3.4.0), unmarked
Imports: ggplot2 (>= 2.0.0), gridExtra, lme4, loo, Matrix, methods,
        Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.0.0),
        stats
Suggests: covr, devtools, knitr, pkgdown, raster, rmarkdown, roxygen2,
        testthat
VignetteBuilder: knitr
Description: 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>.
License: GPL (>= 3)
URL: https://kenkellner.com/ubms/
BugReports: https://github.com/kenkellner/ubms/issues
Encoding: UTF-8
RoxygenNote: 7.1.1
Biarch: true
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppArmadillo (>=
        0.9.300.2.0), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1),
        StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Collate: 'RcppExports.R' 'submodel.R' 'response.R' 'inputs.R' 'fit.R'
        'posterior_predict.R' 'posterior_linpred.R' 'fitted.R' 'gof.R'
        'occu.R' 'colext.R' 'missing.R' 'distamp.R' 'fitlist.R'
        'occuRN.R' 'mb_chisq.R' 'multinomPois.R' 'occuTTD.R' 'pcount.R'
        'plot_marginal.R' 'predict.R' 'ranef.R' 'residuals.R'
        'stanmodels.R' 'ubms-package.R' 'ubmsFit-methods.R'
        'ubmsFitList-methods.R' 'umf.R' 'utils.R'
NeedsCompilation: yes
Packaged: 2021-01-15 18:00:03 UTC; ken
Author: Ken Kellner [cre, aut]
Maintainer: Ken Kellner <contact@kenkellner.com>
Repository: CRAN
Date/Publication: 2021-01-19 09:30:03 UTC
