Implements two methods of estimating runs scored in a softball 
    scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical
    distribution using multinomial random simulation.  Scores are based on player-specific input 
    probabilities (out, single, double, triple, walk, and homerun).  Optional inputs include probability
    of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether
    a player is "fast" (e.g. the player could stretch home).  These probabilities may be 
    calculated from common player statistics that are publicly available on team's webpages. 
    Scores are evaluated based on a nine-player lineup and may be used to compare lineups, 
    evaluate base scenarios, and compare the offensive potential of individual players.  
    Manuscript forthcoming.  See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for 
    implementation of discrete Markov chains.

R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.

User-friendly interface utilities for MCMC models via
    Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
    (or distributed) processors for multiple chains, automated control
    of convergence and sample length diagnostics, and evaluation of the
    performance of a model using drop-k validation or against simulated
    data. Template model specifications can be generated using a standard
    lme4-style formula interface to assist users less familiar with the
    BUGS syntax.  A JAGS extension module provides additional distributions
    including the Pareto family of distributions, the DuMouchel prior and
    the half-Cauchy prior.

Lightweight library for rolling windows operations. Package enables
  full control over the window length, window lag and a time indices. With a runner 
  one can apply any R function on a rolling windows. The package eases work with 
  equally and unequally spaced time series.