This function is associated with sm.poisson for the underlying fitting procedure. It performs a Pseudo-Likelihood Ratio Test for the goodness-of-fit of a standard parametric Poisson regression of specified degree in the covariate x.
This functions evaluates the smoothing parameter which is asymptotically optimal for estimating a density function when the underlying distribution is Normal. Data in one, two or three dimensions can be handled.
lcancer
(Package: sm) :
Spatial positions of cases of laryngeal cancer
These data record the spatial positions of cases of laryngeal cancer in the North-West of England between 1974 and 1983, together with the positions of a number of lung cancer patients who were used as controls. The data have been adjusted to preserve anonymity.
This function estimates nonparametrically the mean profile from a matrix y which is assumed to contain repeated measurements (i.e. longitudinal data) from a set of individuals.
mosses
(Package: sm) :
Heavy metals in mosses in Galicia.
Mosses are used as a means of measuring levels of heavy metal concentrations in the atmosphere, since most of the nutrient uptake of the mosses is from the air. This technique for large-scale monitoring of long-range transport processes has been used in Galicia, in North-West Spain, over the last decade, as described by Fernandez et al. (2005). In 2006, in both March and September, measurements of different metals were collected at 148 points lying almost in a regular grid over the region with 15 km spacing in north-south and east-west directions. According to the ecologists' expertise, the period between the two samples, passing from a humid to a dry season, is enough time to guarantee the independence of the observed processes.
This function selects a smoothing parameter for density estimation in one or two dimensions and for nonparametric regression with one or two covariates. Several methods of selection are available.
sm.regression
(Package: sm) :
Nonparametric regression with one or two covariates.
This function creates a nonparametric regression estimate from data consisting of a single response variable and one or two covariates. In two dimensions a perspective, image (image), contour (slice) or rgl plot of the estimated regression surface is produced. A number of other features of the construction of the estimate, and of its display, can be controlled.
This function estimates nonparametrically the autoregression function (conditional mean given the past values) of a time series x, assumed to be stationary.
providing 
.