Description: Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions.
Suggests: testthat, covr, knitr, rmarkdown, irlba

Description: Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

gfortran: apt-get install -y gfortran (deb)

Authors@R: c( person(given = "Lampros", family = "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person(given = "Matthew", family = "Parks", email = "parksmatthewm@gmail.com", role = "ctb", comment = c(Github = "Github Contributor")) )
Matthew Parks [ctb] (Github Contributor)

Title: Kernel Density Estimation for Heaped Data
Depends: R (>= 2.15.0), plyr, evmix, MASS
Description: In self-reported data the user often encounters heaped data, i.e. data which are rounded to a different
degree of coarseness. While this is mostly a minor problem in parametric density estimation the bias can be very large
for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional
parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods.
Additionally varying rounding probabilities (with the true value) and asymmetric rounding is estimable as well.
Packaged: 2015-01-27 09:10:46 UTC; marcusgross

Depends: R (>= 2.15.0), plyr, evmix, MASS, corpcor
Packaged: 2015-04-24 10:19:42 UTC; marcusgross

Title: Kernel Density Estimation for Heaped and Rounded Data
Depends: R (>= 2.15.0), plyr, evmix, MASS, ks, sparr
Description: In self-reported or anonymized data the user often encounters heaped data, i.e. data which are rounded (to a possibly different
degree of coarseness). While this is mostly a minor problem in parametric density estimation the bias can be very large
parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well. Additionally, bivariate non-parametric density estimation for rounded data is supported.
Packaged: 2015-06-12 14:42:15 UTC; marcusgross