Provides several methods for generating density functions based on binned data. Data are assumed to be nonnegative, but the bin widths need not be uniform, and the top bin may be unbounded. 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.) An estimate for the mean of the distribution may be supplied as an optional argument, which greatly improves the reliability of statistics computed from the smoothed density functions. Methods include step function, recursive subdivision, and optimized spline.
|Author||David J. Hunter and McKalie Drown|
|Date of publication||2016-08-12 16:46:49|
|Maintainer||Dave Hunter <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
county_bins: ACS County Income Data, 2006-2010
county_true: ACS County Income Statistics, 2006-2010
rsubbins: Recursive subdivision PDF and CDF fitted to binned data
simcounty: Simulate data to mimic 'county_bins' and 'county_true'
splinebins: Optimized spline PDF and CDF fitted to binned data
stepbins: Step function PDF and CDF fitted to binned data