Estimate confidence intervals for mean, proportion, mean difference 
   for unpaired and paired samples and proportion difference. Plot the confidence 
   intervals. Generate documents explaining the statistical result step by step.

High resolution mass spectrometry yields often large data sets of 
    spectra from compounds which are not present in available libraries. These 
    spectra need to be annotated and interpreted.
    'InterpretMSSpectrum' provides a set of functions to perform such tasks for 
    Electrospray-Ionization and Atmospheric-Pressure-Chemical-Ionization derived 
    data in positive and negative ionization mode.

Compute permutation- based performance measures and create partial
    dependence plots for (cross-validated) 'randomForest' and 'ada' models.

A set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1017/pan.2021.19>.

Generates three inter-related genomic datasets : methylation, gene expression and protein expression.

Provides an R version of the 'InterVA4' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.

Provides an R version of the 'InterVA5' software (<http://www.byass.uk/interva/>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.

Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].

Perform fast and memory efficient time-weighted averaging of values 
    measured over intervals into new arbitrary intervals. 
	This package is useful in the context of data measured or represented
	as constant values over intervals on a one-dimensional discrete axis 
	(e.g. time-integrated averages of a curve over defined periods). 
	This package was written specifically to deal with air 
	pollution data recorded or predicted as averages over sampling periods. 
	Data in this format often needs to be shifted to non-aligned periods 
	or averaged up to periods of longer duration (e.g. averaging data measured over 
	sequential non-overlapping periods to calendar years).