Description: For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above.

Description: Based on function "spm" of SemiPar package fits semiparametric regression models with spatially adaptive penalized splines.
Packaged: Thu Jun 22 21:57:58 2006; root

Packaged: Thu Apr 26 15:24:18 2007; root

Description: Based on the function "spm" of the SemiPar package fits semiparametric regression models with spatially adaptive penalized splines.
Packaged: Mon Jul 30 21:37:10 2007; root

Description: Based on the function "spm" of the SemiPar package fits
semiparametric regression models with spatially adaptive

Title: Adaptive Semiparametric Regression with Simultaneous Confidence
Author: Manuel Wiesenfarth and Tatyana Krivobokova
Depends: SemiPar, MASS, nlme, cluster, mgcv, splines
Description: Based on the function "asp" of the AdaptFit package fits
penalized splines and computes simultaneous confidence bands.

Description: Based on the function "asp" of the AdaptFit package fits semiparametric regression models with spatially adaptive penalized splines and computes simultaneous confidence bands.

Description: Fits semiparametric regression models with spatially adaptive penalized splines and computes simultaneous confidence bands.

Title: Adaptive Semiparametric Additive Regression with Simultaneous
Confidence Bands and Specification Tests
Description: Fits semiparametric additive regression models with spatially adaptive penalized splines and computes simultaneous confidence bands and associated specification (lack-of-fit) tests. Simultaneous confidence bands cover the entire curve with a prescribed level of confidence and allow us to assess the estimation uncertainty for the whole curve. In contrast to pointwise confidence bands, they permit statements about the statistical significance of certain features (e.g. bumps) in the underlying curve.The method allows for handling of spatially heterogeneous functions and their derivatives as well as heteroscedasticity in the data. See Wiesenfarth et al (2012) <doi:10.1080/01621459.2012.682809>.