Existing adaptive design methods in clinical trials. The package
    includes power, stopping boundaries (sample size) calculation functions for
    two-group group sequential designs, adaptive design with coprimary endpoints,
    biomarker-informed adaptive design, etc.

Create 'ggplot2' themes and color palettes.

Functions to fit the binomial and multinomial additive hazard models and to estimate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) and extended by Yokota et al (2017).

Contains tools to fit the additive hazards model to data from a cohort,
    random sampling, two-phase Bernoulli sampling and two-phase finite population sampling,
    as well as calibration tool to incorporate phase I auxiliary information into the
    two-phase data model fitting.  This package provides regression parameter estimates and
    their model-based and robust standard errors. It also offers tools to make prediction of
    individual specific hazards.

Provide addins for 'RStudio'.
  It currently contains 3 addins. The first to add a shortcut for the double pipe. The second is to add a shortcut for the same operator. And the third to simplify the creation of vectors from texts pasted from the computer transfer area.

Browse through a continuously updated list of existing RStudio 
    addins and install/uninstall their corresponding packages.

'RStudio' allows to show and navigate for the outline of a 
    R Markdown file, but not for R Markdown projects with multiple 
    files. For this reason, I have developed several 'RStudio' addins capable 
    of show project outline. Each addin is specialized in showing projects 
    of different types: R Markdown project, 'bookdown' package project 
    and 'LaTeX' project. There is a configuration file that allows you 
    to customize additional searches.

Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
    via 'additive' <doi:10.5281/zenodo.4784245>. 'tidymodels' is a collection of
    packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
    The technical details of 'mgcv' are described in Wood (2017)
    <doi:10.1201/9781315370279>.