binsmooth - Generate PDFs and CDFs from Binned Data
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.