dbacf: Autocovariance Estimation via Difference-Based Methods

Provides methods for (auto)covariance/correlation function estimation in change point regression with stationary errors circumventing the pre-estimation of the underlying signal of the observations. Generic, first-order, (m+1)-gapped, difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) <doi:10.48550/arXiv.1905.04578>. Bias-reducing, second-order, (m+1)-gapped, difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) <doi:10.1111/sjos.12256>. Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) <doi:10.3150/15-BEJ782>. It also includes a general projection-based method for covariance matrix estimation.

Version: 0.2.8
Depends: R (≥ 2.15.3)
Imports: Matrix
Published: 2023-06-29
Author: Inder Tecuapetla-Gómez [aut, cre]
Maintainer: Inder Tecuapetla-Gómez <itecuapetla at conabio.gob.mx>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: dbacf results

Documentation:

Reference manual: dbacf.pdf

Downloads:

Package source: dbacf_0.2.8.tar.gz
Windows binaries: r-devel: dbacf_0.2.8.zip, r-release: dbacf_0.2.8.zip, r-oldrel: dbacf_0.2.8.zip
macOS binaries: r-release (arm64): dbacf_0.2.8.tgz, r-oldrel (arm64): dbacf_0.2.8.tgz, r-release (x86_64): dbacf_0.2.8.tgz

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