Performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.

Creates lowpass filters which are commonly used in ion channel recordings. It supports generation of random numbers that are filtered, i.e. follow a model for ion channel recordings, see <doi:10.1109/TNB.2018.2845126>. Furthermore, time continuous convolutions of piecewise constant signals with the kernel of lowpass filters can be computed.

Solves quadratic programming problems where the Hessian is represented as the product of two matrices. Thanks to Greg Hunt for helping getting this version back on CRAN. The methods in this package are described in: Ormerod, Wand and Koch (2008) "Penalised spline support vector classifiers: computational issues" <doi:10.1007/s00180-007-0102-8>.

Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Niederreiter-Xing sequence <DOI:10.1007/978-3-642-56046-0_30>.

Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Sobol sequence.

Provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.

Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.

Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.

Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.48550/arXiv.2204.10359>.

Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering.  See Einbeck, Tutz and Evers (2005) <doi:10.1007/s11222-005-4073-8> and Ameijeiras-Alonso and Einbeck (2023) <doi:10.1007/s11634-023-00575-1>.