Bayesian Models for Data from Unmarked Animals using 'Stan'
Extract Coefficient Values From a ubmsFit Model
Extract Pointwise Log-likelihood From Model
Extract Samples From a ubmsFit Model
Create a List of ubmsFit Models
Extract Fitted Values
Get Model Runtime
Get Stan Code From Model
Extract y, the Response Variable, From a ubmsFit Model
Check model goodness-of-fit
K-fold Cross-validation of a ubmsFit Model
Leave-one-out Cross Validation
Model Selection For a List of ubmsFit Models
Get Parameter Names From a ubmsFit Model
Get Names of Models in a ubmsFitList
Get number of Posterior Samples Stored in a ubmsFit Model
Plot Marginal Effects of Covariates
Plot Posterior Distributions
Plot Model Residuals
Plot A Map of the State Parameter Based on a Spatial ubms Model
Plot Residuals For All Submodels in a ubmsFit Model
Posterior Distribution of the Linear Predictor
Draw from the posterior predictive distribution
Predict parameter values from a fitted model
Prior distributions
Projected Occupancy Trajectories
Extract Random Effects
Extract Model Residuals
Get Information for a Restricted Spatial Regression Model
Fit the MacKenzie et al. (2003) Dynamic Occupancy Model
Fit the Royle et al. (2004) Distance Sampling Model
Fit the Multinomial-Poisson Mixture Model
Fit the MacKenzie et al. (2002) Occupancy Model
Fit the Occupancy Model of Royle and Nichols (2003)
Fit Time-to-detection Occupancy Models
Fit the N-mixture model of Royle (2004)
Extract a Submodel from a ubmsFit Model
Extract a ubmsSubmodel From a ubmsSubmodelList Object
Extract Summary Statistics from a ubmsFit Model
Markov Chain Traceplots
Turnover Probability
ubms
Extractors for ubmsFitList objects Extract parts of ubmsFitList object...
Widely Applicable Information Criterion (WAIC)
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>.