A utility to quickly obtain clean and tidy college football
    data. Serves as a wrapper around the
    <https://collegefootballdata.com/> API and provides functions to
    access live play by play and box score data from ESPN
    <https://www.espn.com> when available. It provides users the
    capability to access a plethora of endpoints, and supplement that data
    with additional information (Expected Points Added/Win Probability
    added).

Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
    with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
    CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++. For 
    mathematical details and software tutorial, see Mahani and Sharabiani (2019) <DOI:10.18637/jss.v089.i09>.

Package for the analysis of categorical functional data.
  The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>.
  It also provides functions to perform basic statistical analysis on categorical functional data.

Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.

A simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems,
             to break free from the complex computation of existing similarity formulas and the ability to solve big data.