Reproducible, programmatic retrieval of datasets from the
    GESIS Data Archive.  The GESIS Data Archive <https://search.gesis.org>  
    makes available thousands of invaluable datasets, but researchers using
    these datasets are caught in a bind.  The archive's terms and conditions
    bar dissemination of downloaded datasets to third parties, but to ensure 
    that one's work can be reproduced, assessed, and built upon by others, one
    must provide access to the raw data one has employed.  The 'gesisdata'
    package cuts this knot by providing registered users with programmatic,
    reproducible access to GESIS datasets from within 'R'.

Promote access to the GESLA
    <https://gesla787883612.wordpress.com> (Global Extreme Sea Level
    Analysis) dataset, a higher-frequency sea-level record data from all
    over the world. It provides functions to download it entirely, or
    query subsets directly into R, without the need of downloading the
    full dataset. Also, it provides a built-in web-application, so that
    users can apply basic filters to select the data of interest,
    generating informative plots, and showing the selected sites.

Provides a suite of function-building tools centered around a
  (forward) composition operator, %>>>%, which extends the semantics of the
  'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables
  you to construct composite functions that can be inspected and transformed as
  list-like objects. In conjunction with %>>>%, a compact function constructor,
  fn(), and a partial-application constructor, partial(), are also provided;
  both support quasiquotation.

Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).