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np

This is the R package `np' (Nonparametric Kernel Methods for Mixed Datatypes) written and maintained by Jeffrey S. Racine (racinej@mcmaster.ca) and co-authored by Tristen Hayfield (tristen.hayfield@gmail.com)

Installation

You can install the stable version on CRAN:

install.packages('np', dependencies = TRUE)

Or download the zip ball or tar ball, decompress and run R CMD INSTALL on it.

Alternatively, you can install the development version but before doing so Windows users have to first install Rtools, while OS X users have to first install Xcode and the command line tools (in OS X 10.9 or higher, once you have Xcode installed, open a terminal and run xcode-select --install). Note also that versions of e.g. Rtools are paired with versions of R so ensure you have the latest version of R installed prior to commencing this process.

After installing Rtools/Xcode and devtools (via install.packages("devtools")), install the development package using the following command:

library(devtools); install_github('JeffreyRacine/R-Package-np')

Note also that if you wish a fast install without the building of vignettes (or if you do not have TeX installed on your system), add the option build_vignettes=FALSE to the install_github() call.

Note that if you wish to install the MPI-enabled development version of the package (i.e. the package `npRmpi'), you can add the option ref='npRmpi' to the install_github call above presuming that your system has the required MPI subsystem installed (see my homepage for further details).

For more information on this project please visit the maintainer's website (https://www.socialsciences.mcmaster.ca/people/racinej).

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Install

install.packages('np')

Monthly Downloads

6,448

Version

0.60-18

License

GPL

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Last Published

December 10th, 2024

Functions in np (0.60-18)

cps71

Canadian High School Graduate Earnings
oecdpanel

Cross Country Growth Panel
wage1

Cross-Sectional Data on Wages
gradients

Extract Gradients
Italy

Italian GDP Panel
npcmstest

Kernel Consistent Model Specification Test with Mixed Data Types
npcdens

Kernel Conditional Density Estimation with Mixed Data Types
np

Nonparametric Kernel Smoothing Methods for Mixed Data Types
b.star

Compute Optimal Block Length for Stationary and Circular Bootstrap
Engel95

1995 British Family Expenditure Survey
npudens

Kernel Density Estimation with Mixed Data Types
npdeptest

Kernel Consistent Pairwise Nonlinear Dependence Test for Univariate Processes
npudist

Kernel Distribution Estimation with Mixed Data Types
npcdensbw

Kernel Conditional Density Bandwidth Selection with Mixed Data Types
npudensbw

Kernel Density Bandwidth Selection with Mixed Data Types
npcdist

Kernel Conditional Distribution Estimation with Mixed Data Types
npcopula

Kernel Copula Estimation with Mixed Data Types
npdeneqtest

Kernel Consistent Density Equality Test with Mixed Data Types
npcdistbw

Kernel Conditional Distribution Bandwidth Selection with Mixed Data Types
npconmode

Kernel Modal Regression with Mixed Data Types