diff --git a/BayesianTools/R/BayesianTools.R b/BayesianTools/R/BayesianTools.R index 701f39e..bc8d3ab 100644 --- a/BayesianTools/R/BayesianTools.R +++ b/BayesianTools/R/BayesianTools.R @@ -1,26 +1,49 @@ + #' @title BayesianTools #' @name BayesianTools #' @aliases BayesianTools-package #' @docType package #' @useDynLib BayesianTools, .registration = TRUE -#' @description A package with general-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics -#' @details A package with general-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, particularly for process-based models. +#' @description This package contains general-purpose Markov Chain Monte Carlo +#' (MCMC) and Sequential Monte Carlo (SMC) samplers, along with diagnostic +#' functions and plots used for Bayesian statistics. +#' @details This package works particularly for process-based models. #' -#' The package contains 2 central functions, \code{\link{createBayesianSetup}}, which creates a standardized Bayesian setup with likelihood and priors, and \code{\link{runMCMC}}, which allows to run various MCMC and SMC samplers. +#' The package includes two primary functions, \code{\link{createBayesianSetup}}, +#' which creates a standardized Bayesian setup with likelihood and priors, and +#' \code{\link{runMCMC}}, which allows various MCMC and SMC samplers to be run. #' -#' The package can of course also be used for general (non-Bayesian) target functions. +#' Additionally, the package can be used for general (non-Bayesian) target +#' functions. #' -#' To use the package, a first step is to use \code{\link{createBayesianSetup}} to create a BayesianSetup, which usually contains prior and likelihood densities, or in general a target function. +#' To use this package, start by creating a BayesianSetup using +#' \code{\link{createBayesianSetup}}. Generally, a BayesianSetup contains priors +#' and likelihood densities, or in general, a target function. #' -#' Those can be sampled with \code{\link{runMCMC}}, which can call a number of general purpose Metropolis sampler, including the \code{\link{Metropolis}} that allows to specify various popular Metropolis variants such as adaptive and/or delayed rejection Metropolis; two variants of differential evolution MCMC \code{\link{DE}}, \code{\link{DEzs}}, two variants of DREAM \code{\link{DREAM}} and \code{\link{DREAMzs}}, the \code{\link{Twalk}} MCMC, and a Sequential Monte Carlo sampler \code{\link{smcSampler}}. +#' You can sample with \code{\link{runMCMC}} function. This function can call +#' several general +#' purpose Metropolis samplers including the \code{\link{Metropolis}}. This +#' function allows you to specify various popular Metropolis variants such as +#' adaptive and/or delayed rejection Metropolis; two variants of differential +#' evolution MCMC - \code{\link{DE}} and \code{\link{DEzs}}, two variants of DREAM - +#' \code{\link{DREAM}} and \code{\link{DREAMzs}}, the \code{\link{Twalk}} MCMC, +#' and a Sequential Monte Carlo sampler \code{\link{smcSampler}}. #' -#' The output of runMCMC is of class mcmcSampler / smcSampler if one run is performed, or mcmcSamplerList / smcSamplerList if several sampler are run. Various functions are available for plotting, model comparison (DIC, marginal likelihood), or to use the output as a new prior. +#' If a single run is performed, the output of runMCMC is of class mcmcSampler / +#' smcSampler, otherwise, it would be of class mcmcSamplerList / smcSamplerList. +#' To plot, compare models (DIC, marginal likelihood) or use the output as a +#' new prior, several functions are available. #' -#' For details on how to use the packgage, run vignette("BayesianTools", package="BayesianTools"). +#' To learn how to use the package, run the following command: +#' vignette("BayesianTools", package="BayesianTools"). #' -#' To get the suggested citation, run citation("BayesianTools") +#' Run citation("BayesianTools") to cite the package. #' -#' To report bugs or ask for help, post a \href{https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example}{reproducible example} via the BayesianTools \href{https://github.com/florianhartig/BayesianTools/issues}{issue tracker} on GitHub. +#' To report bugs or ask for help, post at +#' \href{https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example}{reproducible example} +#' via the BayesianTools \href{https://github.com/florianhartig/BayesianTools/issues}{issue tracker} +#' on GitHub. #' -#'Acknowledgements: The creation and maintenance of this package profited from funding and collaboration through Cost Action FP 1304 PROFOUND, DFG DO 786/12-1 CONECT, EU FP7 ERA-NET Sumforest REFORCE and Bayklif Project BLIZ. +#'Acknowledgements: The creation and maintenance of this package profited from +#'funding and collaboration through Cost Action FP 1304 PROFOUND, DFG DO 786/12-1 CONECT, EU FP7 ERA-NET Sumforest REFORCE and Bayklif Project BLIZ. NULL \ No newline at end of file diff --git a/BayesianTools/man/BayesianTools.Rd b/BayesianTools/man/BayesianTools.Rd index 9ab049a..53e6969 100644 --- a/BayesianTools/man/BayesianTools.Rd +++ b/BayesianTools/man/BayesianTools.Rd @@ -6,26 +6,48 @@ \alias{BayesianTools-package} \title{BayesianTools} \description{ -A package with general-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics +This package contains general-purpose Markov Chain Monte Carlo +(MCMC) and Sequential Monte Carlo (SMC) samplers, along with diagnostic +functions and plots used for Bayesian statistics. } \details{ -A package with general-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, particularly for process-based models. +This package works particularly for process-based models. -The package contains 2 central functions, \code{\link{createBayesianSetup}}, which creates a standardized Bayesian setup with likelihood and priors, and \code{\link{runMCMC}}, which allows to run various MCMC and SMC samplers. +The package includes two primary functions, \code{\link{createBayesianSetup}}, +which creates a standardized Bayesian setup with likelihood and priors, and +\code{\link{runMCMC}}, which allows various MCMC and SMC samplers to be run. -The package can of course also be used for general (non-Bayesian) target functions. +Additionally, the package can be used for general (non-Bayesian) target +functions. -To use the package, a first step is to use \code{\link{createBayesianSetup}} to create a BayesianSetup, which usually contains prior and likelihood densities, or in general a target function. +To use this package, start by creating a BayesianSetup using +\code{\link{createBayesianSetup}}. Generally, a BayesianSetup contains priors +and likelihood densities, or in general, a target function. -Those can be sampled with \code{\link{runMCMC}}, which can call a number of general purpose Metropolis sampler, including the \code{\link{Metropolis}} that allows to specify various popular Metropolis variants such as adaptive and/or delayed rejection Metropolis; two variants of differential evolution MCMC \code{\link{DE}}, \code{\link{DEzs}}, two variants of DREAM \code{\link{DREAM}} and \code{\link{DREAMzs}}, the \code{\link{Twalk}} MCMC, and a Sequential Monte Carlo sampler \code{\link{smcSampler}}. +You can sample with \code{\link{runMCMC}} function. This function can call +several general +purpose Metropolis samplers including the \code{\link{Metropolis}}. This +function allows you to specify various popular Metropolis variants such as +adaptive and/or delayed rejection Metropolis; two variants of differential +evolution MCMC - \code{\link{DE}} and \code{\link{DEzs}}, two variants of DREAM - +\code{\link{DREAM}} and \code{\link{DREAMzs}}, the \code{\link{Twalk}} MCMC, +and a Sequential Monte Carlo sampler \code{\link{smcSampler}}. -The output of runMCMC is of class mcmcSampler / smcSampler if one run is performed, or mcmcSamplerList / smcSamplerList if several sampler are run. Various functions are available for plotting, model comparison (DIC, marginal likelihood), or to use the output as a new prior. +If a single run is performed, the output of runMCMC is of class mcmcSampler / +smcSampler, otherwise, it would be of class mcmcSamplerList / smcSamplerList. +To plot, compare models (DIC, marginal likelihood) or use the output as a +new prior, several functions are available. -For details on how to use the packgage, run vignette("BayesianTools", package="BayesianTools"). +To learn how to use the package, run the following command: +vignette("BayesianTools", package="BayesianTools"). -To get the suggested citation, run citation("BayesianTools") +Run citation("BayesianTools") to cite the package. -To report bugs or ask for help, post a \href{https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example}{reproducible example} via the BayesianTools \href{https://github.com/florianhartig/BayesianTools/issues}{issue tracker} on GitHub. +To report bugs or ask for help, post at +\href{https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example}{reproducible example} +via the BayesianTools \href{https://github.com/florianhartig/BayesianTools/issues}{issue tracker} +on GitHub. -Acknowledgements: The creation and maintenance of this package profited from funding and collaboration through Cost Action FP 1304 PROFOUND, DFG DO 786/12-1 CONECT, EU FP7 ERA-NET Sumforest REFORCE and Bayklif Project BLIZ. +Acknowledgements: The creation and maintenance of this package profited from +funding and collaboration through Cost Action FP 1304 PROFOUND, DFG DO 786/12-1 CONECT, EU FP7 ERA-NET Sumforest REFORCE and Bayklif Project BLIZ. }