rtemis: Machine Learning and Visualization

# zzz.R
# ::rtemis::
# 2015-9 Efstathios D. Gennatas egenn.github.io

rtenv <- new.env()
.availableCores <- future::availableCores()
rtCores <- getOption("rt.cores", .availableCores)
rtemis.version <- packageVersion("rtemis")
rtHome = getOption("rt.home", Sys.getenv("HOME"))

.onAttach <- function(libname, pkgname) {

  packageStartupMessage(paste0("  .:", pkgname, " ", rtemis.version, ": Welcome, ", Sys.getenv("USER"),
                               "\n  [", sessionInfo()[2], ": Defaulting to ", rtCores, "/", .availableCores,
                               " available cores]\n  Documentation & vignettes: https://rtemis.netlify.com"))

  # Draw a harmonograph if we are in RStudio
  try(if (try(rstudioapi::isAvailable(), silent = TRUE))
    mplot3.harmonograph(text = "rtemis", text.adj = .01, text.padj = -.2, text.col = "#72CDF4"), silent = TRUE)
}


#' \pkg{rtemis}: Machine Learning and Visualization
#'
#' @section Online Documentation and Vignettes:
#' \url{https://rtemis.netlify.com}
#' @section System Setup:
#' There are some options you can define in your .Rprofile (usually found in your home directory),
#' so you do not have to define each time you execute a function.
#' \describe{
#'    \item{rt.theme}{General plotting theme; set to e.g. "light" or "dark"}
#'    \item{ft.fit.theme}{Plotting theme for true vs. fitted; set to e.g. "lightgrid" or "darkgrid"}
#'    \item{rtCores}{Number of cores to use. By default, rtemis will use available cores reported by
#'    future::availableCores(). In shared systems, you should limit this as appropriate.}
#' }
#' @section Visualization:
#' Static graphics are handled using the \code{mplot3} family.
#' Dynamic graphics are handled using the \code{dplot3} family.
#' @section Supervised Learning:
#' Functions for Regression and Classification begin with \code{s.*}.
#' Run \link{modSelect} to get a list of available algorithms
#' The documentation of each supervised learning function indicates in brackets, after the title
#' whether the function supports classification, regression, and survival analysis \code{[C, R, S]}
#' @section Clustering:
#' Functions for Clustering begin with \code{u.*}.
#' Run \link{clustSelect} to get a list of available algorithms
#' @section Decomposition:
#' Functions for Decomposition and Dimensionality reduction begin with \code{d.*}.
#' Run \link{decomSelect} to get a list of available algorithms
#' @section Cross-Decomposition:
#' Functions for Cross-Decomposition begin with \code{x.*}.
#' Run \link{xdecomSelect} to get a list of available algorithms
#' @section Meta-Modeling:
#' Meta models are trained using \code{meta*} functions.
#'
#' @section Notes:
#' Function documentation includes input type (e.g. "String", "Integer", "Float", etc) and
#' range in interval notation where applicable. For example, Float: [0, 1)" means floats between 0 and 1 including 0,
#' but excluding 1
#'
#' For all classification models, the outcome should be provided as a factor, with the first level
#' of the factor being the 'positive' class, if applicable. A character vector supplied as outcome
#' will be converted to factors, where by default the levels are set alphabetically and therefore
#' the positive class may not be set correctly.
#'
#' @docType package
#' @name rtemis-package
#' @import graphics grDevices methods stats utils parallel R6 data.table crayon future
#' @importFrom magrittr "%>%"
#' @importFrom crayon "%+%"

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