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ModalForecast: Parametric Modal ARIMA Models using the SKD Family

Implements parametric modal Autoregressive Integrated Moving Average (ARIMA) models utilizing the Skewed Distribution (SKD) family. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The conditional mode is parameterized and optimized via maximum likelihood using analytical gradients. Includes comprehensive residual diagnostics, robustness options (heavy tails, asymmetry), robust parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix. Methods are described in Galarza, C.E., Lachos, V.H., Cabral, C.R.B., & Castro, L.M. (2017) <doi:10.1002/sta4.140>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: stats, graphics, forecast, ggplot2, gridExtra, scales, grid
Suggests: rmarkdown, testthat (≥ 3.0.0), knitr
Published: 2026-05-12
DOI: 10.32614/CRAN.package.ModalForecast
Author: Christian Galarza [aut, cre]
Maintainer: Christian Galarza <chedgala at espol.edu.ec>
BugReports: https://github.com/chedgala/ModalForecast/issues
License: GPL-3
URL: https://github.com/chedgala/ModalForecast
NeedsCompilation: no
Materials: README, NEWS
In views: TimeSeries
CRAN checks: ModalForecast results

Documentation:

Reference manual: ModalForecast.html , ModalForecast.pdf

Downloads:

Package source: ModalForecast_0.1.0.tar.gz
Windows binaries: r-devel: ModalForecast_0.1.0.zip, r-release: ModalForecast_0.1.0.zip, r-oldrel: ModalForecast_0.1.0.zip
macOS binaries: r-release (arm64): ModalForecast_0.1.0.tgz, r-oldrel (arm64): ModalForecast_0.1.0.tgz, r-release (x86_64): ModalForecast_0.1.0.tgz, r-oldrel (x86_64): ModalForecast_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.