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.