Finds the largest possible regression model that will still converge
    for various types of regression analyses (including mixed models and generalized
    additive models) and then optionally performs stepwise elimination similar to the
    forward and backward effect-selection methods in SAS, based on the change in
    log-likelihood or its significance, Akaike's Information Criterion, the Bayesian
    Information Criterion, the explained deviance, or the F-test of the change in R².

Working with reproducible reports or any other
    similar projects often require to run the script that builds the
    output file in a specified way. 'buildr' can help you organize, modify
    and comfortably run those scripts. The package provides a set of
    functions that interactively guides you through the process and that
    are available as 'RStudio' Addin, meaning you can set up the keyboard
    shortcuts, enabling you to choose and run the desired build script
    with one keystroke anywhere anytime.

A build system based on 'GNU make' that creates and
    maintains (simply) make files in an R session and provides
    GUI debugging support through 'Microsoft Visual Code'.

Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data. See Wang and Wang (2010) <doi:10.2202/1544-6115.1550>.

Given an expression matrix from a bulk sequencing experiment,
        pre-processes it and creates a shiny app for interactive data 
        analysis and visualisation. The app contains quality checks,
        differential expression analysis, volcano and cross plots,
        enrichment analysis and gene regulatory network inference,
        and can be customised to contain more panels by the user.

Multicenter randomized trials involve the collection and analysis of data from numerous study participants across multiple sites. Outliers may be present. To identify outliers, this package examines data at the individual level (univariate and multivariate) and site-level (with and without covariate adjustment). Methods are outlined in further detail in Rigdon et al (to appear).