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 173 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,027 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 19 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.