SLBDD: Statistical Learning for Big Dependent Data

Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book 'Statistical Learning with Big Dependent Data' by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.

Version: 0.0.4
Depends: R (≥ 3.5.0)
Imports: stats, glmnet, corpcor, forecast, gsarima, cluster, fGarch, imputeTS, methods, MASS, MTS, TSclust, tsoutliers, Matrix, matrixcalc, rnn
Published: 2022-04-27
DOI: 10.32614/CRAN.package.SLBDD
Author: Angela Caro [aut], Antonio Elias [aut, cre], Daniel Peña [aut], Ruey S. Tsay [aut]
Maintainer: Antonio Elias <antonioefz91 at>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
In views: TimeSeries
CRAN checks: SLBDD results


Reference manual: SLBDD.pdf


Package source: SLBDD_0.0.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): SLBDD_0.0.4.tgz, r-oldrel (arm64): SLBDD_0.0.4.tgz, r-release (x86_64): SLBDD_0.0.4.tgz, r-oldrel (x86_64): SLBDD_0.0.4.tgz
Old sources: SLBDD archive

Reverse dependencies:

Reverse imports: outliers.ts.oga


Please use the canonical form to link to this page.