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#' @title sits
#' @description Satellite Image Time Series Analysis
#' for Earth Observation Data Cubes
#'
#' @section Purpose:
#'
#' The SITS package provides a set of tools for analysis,
#' visualization and classification of satellite image time series.
#' It includes methods for filtering, clustering, classification,
#' and post-processing.
#'
#' @note
#' The main \code{sits} classification workflow has the following steps:
#' \enumerate{
#' \item{\code{\link[sits]{sits_cube}}: selects a ARD image collection from
#' a cloud provider.}
#' \item{\code{\link[sits]{sits_cube_copy}}: copies an ARD image collection
#' from a cloud provider to a local directory for faster processing.}
#' \item{\code{\link[sits]{sits_regularize}}: create a regular data cube
#' from an ARD image collection.}
#' \item{\code{\link[sits]{sits_apply}}: create new indices by combining
#' bands of a regular data cube (optional).}
#' \item{\code{\link[sits]{sits_get_data}}: extract time series
#' from a regular data cube based on user-provided labelled samples.}
#' \item{\code{\link[sits]{sits_train}}: train a machine learning
#' model based on image time series.}
#' \item{\code{\link[sits]{sits_classify}}: classify a data cube
#' using a machine learning model and obtain a probability cube.}
#' \item{\code{\link[sits]{sits_smooth}}: post-process a probability cube
#' using a spatial smoother to remove outliers and
#' increase spatial consistency.}
#' \item{\code{\link[sits]{sits_label_classification}}: produce a
#' classified map by selecting the label with the highest probability
#' from a smoothed cube.}
#' }
#'
#' @docType package
#' @name sits-package
#' @aliases sits
"_PACKAGE"
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