R/package.R

In ngboostForecast: Probabilistic Time Series Forecasting

#' @title Probabilistic Time Series Forecasting
#' 
#' @description Probabilistic time series forecasting via Natural Gradient 
#' Boosting for Probabilistic Prediction.
#' 
#' @rdname ngboostForecast
#' @docType package
#' @name ngboostForecast
#' @references 
#' 
#' Duan, T et. al. (2019), NGBoost: Natural Gradient Boosting for Probabilistic 
#' Prediction. 
#' @examples  
#' 
#' \dontrun{
#' 
#' library(ngboostForecast)
#' 
#' model <- NGBforecast$new(Dist = Dist("Normal"),
#'                          Base = sklearner(module = "linear_model",
#'                          class = "Ridge"),
#'                          Score = Scores("LogScore"),
#'                          natural_gradient = TRUE,
#'                          n_estimators = 200,
#'                          learning_rate =  0.1,
#'                          minibatch_frac = 1,
#'                          col_sample = 1,
#'                          verbose = TRUE,
#'                          verbose_eval = 100,
#'                          tol = 1e-5)
#' model$fit(y = AirPassengers, seasonal = TRUE, max_lag = 12, xreg = NULL,
#' 
#' early_stopping_rounds = 10L)
#' 
#' fc <- model$forecast(h = 12, level = c(90, 80), xreg = NULL)
#' 
#' autoplot(fc)
#' 
#'}
NULL


ngboost <- NULL
sklearn <- NULL
scores <- NULL

#' @export
.onLoad <- function(libname, pkgname) {
  reticulate::configure_environment(pkgname)
  ngboost <<- reticulate::import("ngboost",delay_load = TRUE)
  sklearn <<- reticulate::import("sklearn",delay_load = TRUE)
  scores <<- reticulate::import("ngboost.scores",delay_load = TRUE)
}