Package index • np

All functions

b.star(): Compute Optimal Block Length for Stationary and Circular Bootstrap
Engel95: 1995 British Family Expenditure Survey
cps71: Canadian High School Graduate Earnings
Italy: Italian GDP Panel
oecdpanel: Cross Country Growth Panel
wage1: Cross-Sectional Data on Wages
dimBS(): Local-Polynomial Basis Dimension Helper
gradients(): Extract Gradients
np np-package: Nonparametric Kernel Smoothing Methods for Mixed Data Types
npcmstest(): Kernel Consistent Model Specification Test with Mixed Data Types
npcdens(): Kernel Conditional Density Estimation with Mixed Data Types
npcdensbw(): Kernel Conditional Density Bandwidth Selection with Mixed Data Types
npcdist(): Kernel Conditional Distribution Estimation with Mixed Data Types
npcdistbw(): Kernel Conditional Distribution Bandwidth Selection with Mixed Data Types
npconmode() predict(<conmode>) plot(<conmode>): Kernel Modal Regression with Mixed Data Types
npcopula() predict(<npcopula>) plot(<npcopula>): Kernel Copula Estimation with Mixed Data Types
npdeneqtest(): Kernel Consistent Density Equality Test with Mixed Data Types
npudens(): Kernel Density Estimation with Mixed Data Types
npudensbw(): Kernel Density Bandwidth Selection with Mixed Data Types
npdeptest(): Kernel Consistent Pairwise Nonlinear Dependence Test for Univariate Processes
npudist(): Kernel Distribution Estimation with Mixed Data Types
npudistbw(): Kernel Distribution Bandwidth Selection with Mixed Data Types
np.kernels: Kernel Functions Used In np
npksum(): Kernel Sums with Mixed Data Types
np.options: Global Package Options for np
np.pairs() np.pairs.plot(): Cross-Validated Pairs Plot (Helper Functions)
plot(<bandwidth>) plot(<conbandwidth>) plot(<plbandwidth>) plot(<rbandwidth>) plot(<scbandwidth>) plot(<sibandwidth>): General Purpose Plotting of Nonparametric Objects
np_boot_control() np_grid_control() np_render_control(): Validated plot control helpers
npplreg(): Partially Linear Kernel Regression with Mixed Data Types
npplregbw(): Partially Linear Kernel Regression Bandwidth Selection with Mixed Data Types
npqcmstest(): Kernel Consistent Quantile Regression Model Specification Test with Mixed Data Types
npqreg() predict(<qregression>) plot(<qregression>): Kernel Quantile Regression with Mixed Data Types
npquantile(): Kernel Univariate Quantile Estimation
npreg(): Kernel Regression with Mixed Data Types
npregbw(): Kernel Regression Bandwidth Selection with Mixed Data Types
npregiv(): Nonparametric Instrumental Regression
npregivderiv(): Nonparametric Instrumental Derivatives
npsdeptest(): Kernel Consistent Serial Dependence Test for Univariate Nonlinear Processes
npsigtest(): Kernel Regression Significance Test with Mixed Data Types
npindex(): Semiparametric Single Index Model
npindexbw(): Semiparametric Single Index Model Parameter and Bandwidth Selection
npscoef(): Smooth Coefficient Kernel Regression
npscoefbw(): Smooth Coefficient Kernel Regression Bandwidth Selection
npsymtest(): Kernel Consistent Density Asymmetry Test with Mixed Data Types
npunitest(): Kernel Consistent Univariate Density Equality Test with Mixed Data Types
npcdenshat(): Conditional Density Hat Operator
npcdisthat(): Conditional Distribution Hat Operator
nplsqreg(): Location-Scale Kernel Quantile Regression with Mixed Data Types
nplsqregbw(): Bandwidth Selection for Location-Scale Kernel Quantile Regression
npreghat() predict(<npreghat>): Nonparametric Regression Hat Operator
npseed(): Set Random Seed
npindexhat() npplreghat() npscoefhat(): Experimental Hat Operators for Semiparametric Estimators
nptgauss(): Truncated Second-order Gaussian Kernels
npudenshat(): Unconditional Density Hat Operator
npudisthat(): Unconditional Distribution Hat Operator
npuniden.boundary(): Kernel Bounded Univariate Density Estimation Via Boundary Kernel Functions
npuniden.reflect(): Kernel Bounded Univariate Density Estimation Via Data-Reflection
npuniden.sc(): Kernel Shape Constrained Bounded Univariate Density Estimation
se(): Extract Standard Errors
uocquantile(): Compute Quantiles