Package: np 0.60-17

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, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.

Authors:Jeffrey S. Racine [aut, cre], Tristen Hayfield [aut]

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# Install 'np' in R:
install.packages('np', repos = c('https://jeffreyracine.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jeffreyracine/r-package-np/issues

Datasets:
  • Engel95 - 1995 British Family Expenditure Survey
  • Italy - Italian GDP Panel
  • bw - Cross Country Growth Panel
  • bw.all - Cross-Sectional Data on Wages
  • bw.subset - Cross-Sectional Data on Wages
  • cps71 - Canadian High School Graduate Earnings
  • oecdpanel - Cross Country Growth Panel
  • wage1 - Cross-Sectional Data on Wages

On CRAN:

12.10 score 47 stars 40 packages 656 scripts 5.7k downloads 11 mentions 41 exports 11 dependencies

Last updated 3 months agofrom:ba7840be9e. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-win-x86_64NOTENov 03 2024
R-4.5-linux-x86_64NOTENov 03 2024
R-4.4-win-x86_64OKNov 03 2024
R-4.4-mac-x86_64OKNov 03 2024
R-4.4-mac-aarch64OKNov 03 2024
R-4.3-win-x86_64OKNov 03 2024
R-4.3-mac-x86_64OKNov 03 2024
R-4.3-mac-aarch64OKNov 03 2024

Exports:b.stargradientsnpcdensnpcdensbwnpcdistnpcdistbwnpcmstestnpconmodenpcopulanpdeneqtestnpdeptestnpindexnpindexbwnpksumnpplotnpplregnpplregbwnpqcmstestnpqregnpquantilenpregnpregbwnpregivnpregivderivnpscoefnpscoefbwnpsdeptestnpseednpsigtestnpsymtestnptgaussnpudensnpudensbwnpudistnpudistbwnpuniden.boundarynpuniden.reflectnpuniden.scnpunitestseuocquantile

Dependencies:bootcubaturelatticeMASSMatrixMatrixModelsquadprogquantregRcppSparseMsurvival

Entropy-based Inference Using the np Package

Rendered fromentropy_np.Rnwusingutils::Sweaveon Nov 03 2024.

Last update: 2022-08-12
Started: 2013-11-01

Frequently Asked Questions (np)

Rendered fromnp_faq.Rnwusingutils::Sweaveon Nov 03 2024.

Last update: 2023-03-12
Started: 2013-11-01

The np Package

Rendered fromnp.Rnwusingutils::Sweaveon Nov 03 2024.

Last update: 2022-08-12
Started: 2013-11-01

Readme and manuals

Help Manual

Help pageTopics
Compute Optimal Block Length for Stationary and Circular Bootstrapb.star
Canadian High School Graduate Earningscps71
1995 British Family Expenditure SurveyEngel95
Extract Gradientsgradients gradients.condensity gradients.condistribution gradients.npregression gradients.qregression gradients.singleindex
Italian GDP PanelItaly
Nonparametric Kernel Smoothing Methods for Mixed Data Typesnp-package np
Kernel Conditional Density Estimation with Mixed Data Typesnpcdens npcdens.call npcdens.conbandwidth npcdens.default npcdens.formula
Kernel Conditional Density Bandwidth Selection with Mixed Data Typesnpcdensbw npcdensbw.conbandwidth npcdensbw.default npcdensbw.formula npcdensbw.NULL
Kernel Conditional Distribution Estimation with Mixed Data Typesnpcdist npcdist.call npcdist.condbandwidth npcdist.default npcdist.formula
Kernel Conditional Distribution Bandwidth Selection with Mixed Data Typesnpcdistbw npcdistbw.condbandwidth npcdistbw.default npcdistbw.formula npcdistbw.NULL
Kernel Consistent Model Specification Test with Mixed Data Typesnpcmstest
Kernel Modal Regression with Mixed Data Typesnpconmode npconmode.call npconmode.conbandwidth npconmode.default npconmode.formula
Kernel Copula Estimation with Mixed Data Typesnpcopula
Kernel Consistent Density Equality Test with Mixed Data Typesnpdeneqtest
Kernel Consistent Pairwise Nonlinear Dependence Test for Univariate Processesnpdeptest
Semiparametric Single Index Modelnpindex npindex.call npindex.default npindex.formula npindex.sibandwidth
Semiparametric Single Index Model Parameter and Bandwidth Selectionnpindexbw npindexbw.default npindexbw.formula npindexbw.NULL npindexbw.sibandwidth
Kernel Sums with Mixed Data Typesnpksum npksum.default npksum.formula npksum.numeric
General Purpose Plotting of Nonparametric Objectsnpplot npplot.bandwidth npplot.conbandwidth npplot.plbandwidth npplot.rbandwidth npplot.scbandwidth npplot.sibandwidth
Partially Linear Kernel Regression with Mixed Data Typesnpplreg npplreg.call npplreg.formula npplreg.plbandwidth
Partially Linear Kernel Regression Bandwidth Selection with Mixed Data Typesnpplregbw npplregbw.default npplregbw.formula npplregbw.NULL npplregbw.plbandwidth
Kernel Consistent Quantile Regression Model Specification Test with Mixed Data Typesnpqcmstest
Kernel Quantile Regression with Mixed Data Typesnpqreg npqreg.call npqreg.condbandwidth npqreg.default npqreg.formula
Kernel Univariate Quantile Estimationnpquantile
Kernel Regression with Mixed Data Typesnpreg npreg.call npreg.default npreg.formula npreg.rbandwidth
Kernel Regression Bandwidth Selection with Mixed Data Typesnpregbw npregbw.default npregbw.formula npregbw.NULL npregbw.rbandwidth
Nonparametric Instrumental Regressionnpregiv
Nonparametric Instrumental Derivativesnpregivderiv
Smooth Coefficient Kernel Regressionnpscoef npscoef.call npscoef.default npscoef.formula npscoef.scbandwidth
Smooth Coefficient Kernel Regression Bandwidth Selectionnpscoefbw npscoefbw.default npscoefbw.formula npscoefbw.NULL npscoefbw.scbandwidth
Kernel Consistent Serial Dependence Test for Univariate Nonlinear Processesnpsdeptest
Set Random Seednpseed
Kernel Regression Significance Test with Mixed Data Typesnpsigtest npsigtest.call npsigtest.default npsigtest.formula npsigtest.npregression npsigtest.rbandwidth
Kernel Consistent Density Asymmetry Test with Mixed Data Typesnpsymtest
Truncated Second-order Gaussian Kernelsnptgauss
Kernel Density Estimation with Mixed Data Typesnpudens npudens.bandwidth npudens.call npudens.default npudens.formula
Kernel Density Bandwidth Selection with Mixed Data Typesnpudensbw npudensbw.bandwidth npudensbw.default npudensbw.formula npudensbw.NULL
Kernel Distribution Estimation with Mixed Data Typesnpudist npudist.call npudist.dbandwidth npudist.default npudist.formula
Kernel Distribution Bandwidth Selection with Mixed Data Typesnpudistbw npudistbw.dbandwidth npudistbw.default npudistbw.formula npudistbw.NULL
Kernel Bounded Univariate Density Estimation Via Boundary Kernel Functionsnpuniden.boundary
Kernel Bounded Univariate Density Estimation Via Data-Reflectionnpuniden.reflect
Kernel Shape Constrained Bounded Univariate Density Estimationnpuniden.sc
Kernel Consistent Univariate Density Equality Test with Mixed Data Typesnpunitest
Cross Country Growth Panelbw oecdpanel
Extract Standard Errorsse
Compute Quantilesuocquantile
Cross-Sectional Data on Wagesbw.all bw.subset wage1