unique_tol

Get unique values in a sorted numeric vector, where comparison is up to a
specified numeric tolerance. If there is a run of values where each
consecutive pair is closer together than the tolerance, all are labeled as
corresponding to a single unique value even if not all values in the run are
within the tolerance.

Given a set of predictive quantiles from a distribution,
estimate the distribution and create `d`, `p`, `q`, and `r` functions
to evaluate its density function, distribution function, and quantile
function, and generate random samples. On the interior of the provided
quantiles, an interpolation method such as a monotonic cubic spline is
used; the tails are approximated by a location-scale family.

Evan Ray

distfromq

Reconstruct a Distribution from a Collection of Quantiles

Aaron Gerding

Li Shandross

Nick Reich

unique_tol function

<dl><dt>x</dt>
<dd>a numeric vector in which to identify duplicates</dd>
<dt>tol</dt>
<dd>numeric tolerance for identifying duplicates</dd>
<dt>ties</dt>
<dd>a function that is used to summarize groups of values that fall
within the tolerance</dd></dl>

Arguments

Get unique values in a sorted numeric vector, where comparison is up to a
specified numeric tolerance. If there is a run of values where each
consecutive pair is closer together than the tolerance, all are labeled as
corresponding to a single unique value even if not all values in the run are
within the tolerance. — unique_tol

<dl>

<dt>x</dt>
<dd>a numeric vector in which to identify duplicates</dd>


<dt>tol</dt>
<dd>numeric tolerance for identifying duplicates</dd>


<dt>ties</dt>
<dd>a function that is used to summarize groups of values that fall
within the tolerance</dd>

</dl>

Get unique values in a sorted numeric vector, where comparison is up to a
specified numeric tolerance. If there is a run of values where each
consecutive pair is closer together than the tolerance, all are labeled as
corresponding to a single unique value even if not all values in the run are
within the tolerance.

Description

Usage

Value

Arguments