An interface to simplify organizing parameters used in a package,
    using external configuration files. This attempts to provide a cleaner
    alternative to options().

This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.

Run simulations or other functions while easily varying parameters
    from one iteration to the next. Some common use cases would be grid search
    for machine learning algorithms, running sets of simulations (e.g.,
    estimating statistical power for complex models), or bootstrapping under
    various conditions. See the 'paramtest' documentation for more information
    and examples.

An implementation of Horn's technique for numerically and graphically evaluating the components or factors retained in a principle components analysis (PCA) or common factor analysis (FA). Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention.

Fast, flexible framework for implementing Bayesian optimization of model 
	hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>.
	The package allows the user to run scoring function in parallel, save intermediary 
	results, and tweak other aspects of the process to fully utilize the computing resources
	available to the user.