Cut Numeric Values into Evenly Distributed Groups
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.

Efficient Implementation of Binary Segmentation
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.

covr, directlabels, ggplot2, testthat, knitr, markdown,
neuroblastoma

covr, penaltyLearning, directlabels, ggplot2, testthat,
knitr, markdown, neuroblastoma, changepoint, quadprog

Exact Binary Sequential Designs and Analysis
For a series of binary responses, create stopping boundary
with exact results after stopping, allowing updating for
missing assessments.

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. 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.

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,
but the bin widths need not be equal, and the top bin need not have an
upper bound. 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. 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
functions only support splinebins.