kdensity

Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) doi:10.1214/aos/1176324627. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) doi:10.1023/A:1004165218295, the beta kernel of Chen (1999) doi:10.1016/S0167-9473(99)00010-9, and the copula kernel of Jones & Henderson (2007) doi:10.1093/biomet/asm068. User-supplied kernels, parametric starts, and bandwidths are supported.

Last 30 days

This package has been downloaded 578 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 27 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 5,966 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Aug 27, 2024 with 133 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.