bayesPO (version 0.5.0)

Bayesian Inference for Presence-Only Data

Description

Presence-Only data is best modelled with a Point Process Model. The work of Moreira and Gamerman (2022) provides a way to use exact Bayesian inference to model this type of data, which is implemented in this package.

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Version

Install

install.packages('bayesPO')

Monthly Downloads

199

Version

0.5.0

License

GPL-3

Maintainer

Guido Alberti Moreira

Last Published

February 1st, 2024

Functions in bayesPO (0.5.0)

NormalPrior-class

Normal prior class for Beta and Delta parameters.

GammaPrior

Create a Gamma prior object for model specification.

NormalPrior

Create a Normal prior object for model specification.

GammaPrior-class

Gamma prior class for the LambdaStar parameter.

bayesPO_model-class

Class that defines a model for the bayesPO package.

BetaDeltaPrior-class

Generic class for the beta and delta parameters.

LambdaStarPrior-class

Generic class for the LambdaStar parameters.

bayesPO_initial-class

Class for the initial values for the MCMC for the bayesPO package

bayesPO_fit-class

Class for the result of the MCMC procedure.

prior

Build a joint prior for bayesPO model parameters

bayesPO_model

Build a model to be used in the bayesPO fitting function

bayesPO_prior-class

Joint prior class for the bayesPO package parameters

fit_bayesPO

Fit presence-only data using a Bayesian Poisson Process model

covariates_importance-class

Class for covariates importance matrices

initial

Initial values constructor for bayesPO modeling