# zzz.R
# ::rtemis::
# 2016-23 E.D. Gennatas www.lambdamd.org
rtenv <- new.env()
rtenv$binclasspos <- 1
rtemis.version <- packageVersion("rtemis")
.availableCores <- future::availableCores()
.onLoad <- function(libname, pkgname) {
# Defaults ----
rtPlan <- rtPlanInit()
assign("rtPlan", rtPlan, envir = parent.env(environment()))
rtCores <- rtCoresInit()
assign("rtCores", rtCores, envir = parent.env(environment()))
rtTheme <- rtThemeInit()
assign("rtTheme", rtTheme, envir = parent.env(environment()))
rtFont <- rtFontInit()
assign("rtFont", rtFont, envir = parent.env(environment()))
rtPalette <- rtPaletteInit()
assign("rtPalette", rtPalette, envir = parent.env(environment()))
rtDate <- rtDateInit() == "TRUE"
assign("rtDate", rtDate, envir = parent.env(environment()))
}
.onAttach <- function(libname, pkgname) {
if (interactive()) {
packageStartupMessage(paste0(
rtasciitxt(),
" .:", pkgname, " ", rtemis.version, " \U1F30A", " ", sessionInfo()[[2]],
bold("\n Defaults"),
"\n \u2502 ", italic(gray("Theme: ")), rtTheme,
"\n \u2502 ", italic(gray("Font: ")), rtFont,
"\n \u2502 ", italic(gray("Palette: ")), rtPalette,
"\n \u2502 ", italic(gray("Plan: ")), rtPlan,
"\n \u2514 ", italic(gray("Cores: ")), rtCores, "/", .availableCores, " available",
bold("\n Resources"),
"\n \u2502 ", italic(gray("Docs:")), " https://rtemis.lambdamd.org",
"\n \u2502 ", italic(gray("Learn R:")), " https://class.lambdamd.org/pdsr",
"\n \u2502 ", italic(gray("Themes:")), " https://egenn.lambdamd.org/software/#rtemis_themes",
"\n \u2514 ", italic(gray("Cite:")), ' > citation("rtemis")',
bold("\n Setup"),
"\n \u2514 ", italic(gray("Enable progress reporting:")),
" > progressr::handlers(global = TRUE)",
'\n > progressr::handlers("cli")'
))
} else {
packageStartupMessage(
paste0(
" .:", pkgname, " ", rtemis.version, " \U1F30A", " ", sessionInfo()[[2]]
)
)
}
}
#' \pkg{rtemis}: Machine Learning and Visualization
#'
#' @description
#' Advanced Machine Learning made easy, efficient, reproducible
#'
#' @section Online Documentation and Vignettes:
#' <https://rtemis.lambdamd.org>
#' @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. "whiteigrid" or "darkgraygrid"}
#' \item{rt.palette}{Name of default palette to use in plots. See options by running `rtpalette()`}
#' \item{rt.font}{Font family to use in plots.}
#' \item{rt.cores}{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.}
#' \item{future.plan}{Default plan to use for parallel processing.}
#' }
#' @section Visualization:
#' Static graphics are handled using the `mplot3` family.
#' Dynamic graphics are handled using the `dplot3` family.
#' @section Supervised Learning:
#' Functions for Regression and Classification begin with `s_*`.
#' Run [select_learn] 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 `[C, R, S]`
#' @section Clustering:
#' Functions for Clustering begin with `c_*`.
#' Run [select_clust] to get a list of available algorithms
#' @section Decomposition:
#' Functions for Decomposition and Dimensionality reduction begin with
#' `d_*`.
#' Run [select_decom] to get a list of available algorithms
#' @section Cross-Decomposition:
#' Functions for Cross-Decomposition begin with `x_*`.
#' Run [xselect_decom] to get a list of available algorithms
#' @section Meta-Modeling:
#' Meta models are trained using `meta*` functions.
#'
#' @section Notes:
#' Function documentation includes input type (e.g. "String", "Integer",
#' "Float"/"Numeric", 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.
#'
# @useDynLib rtemis, .registration = TRUE
# @importFrom Rcpp evalCpp
#' @name rtemis-package
#' @import graphics grDevices methods stats utils data.table R6 future htmltools
"_PACKAGE"
NULL
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