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: | 0.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, glmnet, corpcor, forecast, gsarima, cluster, fGarch, imputeTS, methods, MASS, MTS, TSclust, tsoutliers, Matrix, matrixcalc, rnn |
| Published: | 2021-02-16 |
| Author: | Angela Caro [aut], Antonio Elias [aut, cre], Daniel Peña [aut], Ruey S. Tsay [aut] |
| Maintainer: | Antonio Elias <antonioefz91 at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | SLBDD results |
| Reference manual: | SLBDD.pdf |
| Package source: | SLBDD_0.0.1.tar.gz |
| Windows binaries: | r-devel: SLBDD_0.0.1.zip, r-release: SLBDD_0.0.1.zip, r-oldrel: SLBDD_0.0.1.zip |
| macOS binaries: | r-release: SLBDD_0.0.1.tgz, r-oldrel: SLBDD_0.0.1.tgz |
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