Description: Fit profile analytic models. This includes multivariate methods and data visualization tools
Author: Okan Bulut <okanbulut84@gmail.com>, Christopher David Desjardins <cddesjardins@gmail.com>,

Description: Allows users to quickly and reliably profile data in R
using convenience functions. The profiled data returns as a data.frame
and provides a wealth of common and uncommon summary statistics.
Packaged: 2015-09-09 16:49:18 UTC; phendricks

Description: A simple and light-weight API for memory profiling of R expressions.  The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).

Title: Probabilistic Forecast Combination Using CRPS Learning
Description: Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <arXiv:1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, splines2 (>= 0.3.0)
Suggests: testthat (>= 3.0.0), gamlss.dist, ggplot2
Author: Jonathan Berrisch [cre] (<https://orcid.org/0000-0002-4944-9074>),

LinkingTo: Rcpp, RcppArmadillo, RcppProgress, splines2 (>= 0.4.4)

Imports: Rcpp (>= 1.0.5), Matrix, abind, methods
LinkingTo: Rcpp, RcppArmadillo (>= 0.10.7.5.0), RcppProgress, splines2

Description: Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <arXiv:1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.