Functions for computing: (1) the adaptive normal PI estimate for data after the logarithmic transformation; (2) single-bandwidth PI density estimate for data after the logarithmic transformation; (3) single bandwidth PI estimate for data after the power transformation. See the articles: (1) Savchuk, O. (2026, under review). Density estimation for log-transformed data; (2) Savchuk, O., Schick A. (2013). Density estimation for power transformations. Journal of Nonparametric Statistics, 25(3), 545-559 <doi:10.1080/10485252.2013.811788>.

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020) <arXiv:2009.06182>.

Provides various tools for analysing density profiles
    obtained by resistance drilling. It can load individual or
    multiple files and trim the starting and ending part of each
    density profile. Tools are also provided to trim profiles
    manually, to remove the trend from measurements using several
    methods, to plot the profiles and to detect tree rings
    automatically. Written with a focus on forestry use of resistance
    drilling in standing trees.

With bivariate data, it is possible to calculate
    2-dimensional kernel density estimates that return polygons at given
    levels of probability. 'densityarea' returns these polygons for
    analysis, including for calculating their area.

An improved implementation (based on k-nearest neighbors) of
    the density peak clustering algorithm, originally described by Alex
    Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large 
    datasets (> 100,000 samples) very efficiently. It was initially implemented 
    by Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by 
    Xiaojie Qiu to handle large datasets with kNNs.