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.
|stats, pracma, ineq, triangle
|David J. Hunter and McKalie Drown
|Dave Hunter <dhunter at westmont.edu>
|MIT + file LICENSE
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