OOS: Out-of-Sample Time Series Forecasting

A comprehensive and cohesive API for the out-of-sample forecasting workflow: data preparation, forecasting - including both traditional econometric time series models and modern machine learning techniques - forecast combination, model and error analysis, and forecast visualization.

Version: 1.0.0
Depends: R (≥ 4.0.0)
Imports: caret, dplyr, forecast, furrr, future, ggplot2, glmnet, imputeTS, lmtest, lubridate, magrittr, purrr, sandwich, stats, tidyr, vars, xts, zoo
Suggests: knitr, testthat, rmarkdown, quantmod
Published: 2021-03-17
Author: Tyler J. Pike [aut, cre]
Maintainer: Tyler J. Pike <tjpike7 at gmail.com>
BugReports: https://github.com/tylerJPike/OOS/issues
License: GPL-3
URL: https://github.com/tylerJPike/OOS, https://tylerjpike.github.io/OOS/
NeedsCompilation: no
Materials: README
CRAN checks: OOS results

Downloads:

Reference manual: OOS.pdf
Vignettes: Window functions
Package source: OOS_1.0.0.tar.gz
Windows binaries: r-devel: OOS_1.0.0.zip, r-release: OOS_1.0.0.zip, r-oldrel: OOS_1.0.0.zip
macOS binaries: r-release (arm64): OOS_1.0.0.tgz, r-release (x86_64): OOS_1.0.0.tgz, r-oldrel: OOS_1.0.0.tgz

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