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.

Provides a nonparametric smoothed kernel density estimator for the future conditional hazard when time-dependent covariates are present. It also provides pointwise and uniform confidence bands and a bandwidth selection.

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.

Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.

Transform and analyze workforce data in meaningful ways for human resources (HR) analytics. Get started with workforce planning using a simple Shiny app.

A compilation of extra 'ggplot2' themes, scales and utilities, including a 
    spell check function for plot label fields and an overall emphasis on typography. 
    A copy of the 'Google' font 'Roboto Condensed' is also included.

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).

A program that conducts group variable selection for quantile and robust mean 
              regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for
              group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss.
              Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check 
              function can be approximated by the Huber loss for the median and the tilted version of 
              Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and
              has also been shown to be statistical consistent.