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.

Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" <arXiv:2104.12597>.