An implementation of high-probability lower bounds for the total variance distance 
             as introduced in Michel & Naef & Meinshausen (2020) <arXiv:2005.06006>. An estimated 
             lower-bound (with high-probability) on the total variation distance between two probability distributions from which
             samples are observed can be obtained with the function HPLB.

Package that simplifies the use of the 'HPZone' API. Most of the annoying and labor-intensive parts of the interface are handled by wrapper functions. Note that the API and its details are not publicly available. Information can be found at <https://www.ggdghorkennisnet.nl/groep/726-platform-infectieziekte-epidemiologen/documenten/map/9609> for those with access.

Provides univariate and indexed (multivariate) nonparametric smoothed kernel estimators for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025)  <doi:10.1093/biomet/asaf008>.

Miscellaneous convenience functions and wrapper functions 
	to convert frequencies between Hz, semitones, mel and Bark, 
	to create a matrix of dummy columns from a factor, 
	to determine whether x lies in range [a,b], 
	and to add a bracketed line to an existing plot.
	This package also contains an example data set of a stratified sample
	of 80 talkers of Dutch.

Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

Methods for data engineering in the human resources (HR) corporate domain. Designed for HR analytics practitioners and workforce-oriented data sets.

Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).