np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <>).

Version: 0.60-6
Imports: boot, cubature, methods, quantreg, stats
Suggests: MASS
Published: 2018-01-13
Author: Jeffrey S. Racine [aut, cre], Tristen Hayfield [aut]
Maintainer: Jeffrey S. Racine <racinej at>
License: GPL-2 | GPL-3 [expanded from: GPL]
Copyright: see file COPYRIGHTS
NeedsCompilation: yes
Citation: np citation info
Materials: README ChangeLog
In views: Econometrics, SocialSciences
CRAN checks: np results


Reference manual: np.pdf
Vignettes: Entropy-based Inference Using the np Package
The np Package
Frequently Asked Questions (np)
Package source: np_0.60-6.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: np_0.60-6.tgz
OS X Mavericks binaries: r-oldrel: np_0.60-5.tgz
Old sources: np archive

Reverse dependencies:

Reverse depends: causalweight, CovSel, DepthProc, generalCorr, rddtools, semsfa, stam
Reverse imports: analytics, anchoredDistr, CARS, Compind, condSURV, crs, drtmle, npbr, nse, NSM3, rpatrec, survidm, treeclim
Reverse suggests: AER, BNSP, mlt.docreg, spaero


Please use the canonical form to link to this page.