R/paramonte-package.R

In ShashankKumbhare/paramonteR: Plain Powerful Parallel Monte Carlo Library

packageName = "paramonte"

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################################## paramonte-package-doc ###########################################################################
# >>
#'
#' @details
#'
#-----------------------------------------------------------------------------------------------------------------------------------
#' \describe{\item{_**What is ParaMonte?**_}{
#-----------------------------------------------------------------------------------------------------------------------------------
#'
#' ParaMonte is a serial / parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-
#' dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific
#' inference, with the design goal of unifying the
#'
#' - **automation** of Monte Carlo simulations,
#' - **user-friendliness** of the library,
#' - **accessibility** from multiple programming environments,
#' - **high-performance** at runtime, and,
#' - **scalability** across many parallel processors.
#'
#' For more information on the installation, usage, and examples, visit:
#'
#' - <https://www.cdslab.org/paramonte>
#'
#' For the API documentation, visit:
#'
#' - <https://www.cdslab.org/paramonte/notes/api/python>
#'
#' }}
#'
#' \out{<hr>}
#'
#-----------------------------------------------------------------------------------------------------------------------------------
#' \describe{\item{_**ParaMonte samplers**_}{
#-----------------------------------------------------------------------------------------------------------------------------------
#'
#' The routines currently supported by the ParaMonte Python library include:
#'
#'   \describe{\item{**[ParaDRAM]**}{
#'
#'   Parallel Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo Sampler. For a quick start, example scripts,
#'   and instructions on how to use the [ParaDRAM] sampler, type the following commands enclosed between the comment lines in your
#'   R session,
#'
#'   ```
#'   pm = paramonte_class$new()
#'   help("ParaDRAM")           # the input value is case-insensitive
#'   ```
#'
#'   or,
#'
#'   ```
#'   pm = paramonte_class$new()
#'   ?ParaDRAM                  # get help on ParaDRAM sampler class
#'   ````
#'
#'   }}
#'
#' }}
#'
#' \out{<hr>}
#'
#-----------------------------------------------------------------------------------------------------------------------------------
#' ## Naming conventions
#-----------------------------------------------------------------------------------------------------------------------------------
#'
#' - The camelCase naming style is used throughout the entire ParaMonte library, across all programming languages. The ParaMonte
#'   library is a multi-language cross-platform library. To increase the consistently and similarities of all implementations, a
#'   single naming convention had to be used for all different languages.
#'
#' - All simulation specifications start with a lowercase letter, including scalar/vector/matrix int, float, string, or boolean variables.
#'
#' - The name of any variable that represents a vector of values is normally suffixed with `Vec`, for example: `startPointVec`, ...
#'
#' - The name of any variable that represents a matrix of values is normally suffixed with `Mat`, for example: `proposalStartCorMat`, ...
#'
#' - The name of any variable that represents a list of varying-size values is normally suffixed with `List`, like: `variableNameList`, ...
#'
#' - All static functions or methods of classes begin with a lowercase verb.
#'
#' - Significant attempt has been made to end all boolean variables with a passive verb, such that the full variable name virtually
#'   forms a proposition, that is, an English-language statement that should be either `TRUE` or `FALSE`, set by the user.
#'
#' \out{<hr>}
#'
#-----------------------------------------------------------------------------------------------------------------------------------
#' ## Tips
#-----------------------------------------------------------------------------------------------------------------------------------
#'
#' - When running the ParaMonte samplers, in particular on multiple cores in parallel, it would be best to close any such aggressive
#'   software or applications as **Dropbox**, **ZoneAlarm**, ... that can interfere with the ParaMonte simulation output files,
#'   potentially causing the sampler to crash before the successful completion of the simulation. These situations should however
#'   happen only scarcely.
#'
#' - On Windows systems, when restarting an old interrupted ParaMonte simulation, ensure your Python session is also restarted before
#'   the simulation restart. This may be needed as Windows sometimes locks access to some or all of the simulation output files.
#'
#' - To unset an already-set input simulation specification, simply set the simulation attribute to None or re-instantiate the sampler.
#'
#' \out{<hr>}
#'
# <<
################################## paramonte-package-doc ###########################################################################
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#'
#' @keywords internal
"_PACKAGE"

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