Smoothing Methods for Nonparametric Regression and Density Estimation
Estimation of the error standard deviation in nonparametric regression...
Construct frequency table from raw data in 1, 2 or 3 dimensions.
Selection of the smoothing parameter
Cross-validatory choice of smoothing parameter
Normal optimal choice of smoothing parameter in density estimation
Sheather-Jones choice of smoothing parameter for density estimation
Integrated squared error between a density estimate and a Normal densi...
mean integrated squared error for density estimation with normal data
nearest neighbour distances from data in one or two dimensions
Pause before continuing execution
Making data available as data.frame
A significance trace for a hypothesis test
Internal sm functions
Nonparametric analysis of covariance
Nonparametric estimation of the autoregression function
Bootstrap goodness-of-fit test for a logistic regression model.
Nonparametric logistic regression
Comparison of univariate density estimates
Nonparametric density estimation in one, two or three dimensions.
The detection of discontinuities in a regression curve or surface.
A test of monotonicity in a regression curve.
Set or return options of sm library
Smooth principal components analysis
Bootstrap goodness-of-fit test for a Poisson regression model
Nonparametric Poisson regression
The sm package: summary information
Nonparametric regression with autocorrelated errors
Nonparametric regression with one or two covariates.
Nonparametric analysis of repeated measurements data
Running a script associated to the sm library
Comparison across two groups of the error standard deviation in nonpar...
Nonparametric density estimation for spherical data.
Adding a regression surface to an rgl plot.
Nonparametric regression with survival data.
Nonparametric density estimation of stationary time series data
Confidence intervals and tests based on smoothing an empirical variogr...
This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.