Implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details.

Implementation of algorithms for cutting numerical values
    exhibiting a potentially highly skewed distribution into evenly distributed
    groups (bins). This functionality can be applied for binning discrete
    values, such as counts, as well as for discretization of continuous values,
    for example, during generation of features used in machine learning
    algorithms.

Provides methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point.

Standard template library 
 containers are used to implement an efficient binary segmentation
 algorithm, which is log-linear on average and quadratic in the
 worst case.

For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.

Provides several methods for generating density functions
    based on binned data. Methods include step function, recursive
    subdivision, and optimized spline. Data are assumed to be nonnegative, 
    the top bin is assumed to have no upper bound, but the bin widths need
    be equal. All PDF smoothing methods maintain the areas specified by 
    the binned data. (Equivalently, all CDF smoothing methods interpolate 
    the points specified by the binned data.) In practice, an estimate for 
    the mean of the distribution should be supplied as an optional argument.
    Doing so greatly improves the reliability of statistics computed from 
    the smoothed density functions. Includes methods for estimating the Gini 
    coefficient, the Theil index, percentiles, and random deviates from a 
    smoothed distribution. Among the three methods, the optimized spline 
    (splinebins) is recommended for most purposes. The percentile and 
    random-draw methods should be regarded as experimental, and these methods 
    only support splinebins.