dbacf

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.

Last 30 days

This package has been downloaded 178 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

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Last 365 days

This package has been downloaded 2,037 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 19 downloads.

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