Functions for cosmological distances, times, luminosities, etc.
Package encapsulates standard expressions for distances,
times, luminosities, and other quantities useful in
observational cosmology, including molecular line observations.
Currently coded for a flat universe only.

Functions for Cosmological Distances, Times, Luminosities, Etc
Package encapsulates standard expressions for distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations.  Currently coded for a flat universe only.

This package aims at the easy production of descriptive and
bivariate statistics tables in HTML. It can load files in csv, xls/xlsx
formats, and read labels and formats from SAS or plain text files. It is
intended to be usable by absolute R beginners. Ce paquet permet la
production facile de tableaux HTML descriptifs et comparatifs, meme pour
les debutants sous R.

COSSO, adaptive COSSO, variable selection for nonparametric
smoothing spline models.
Hao Helen Zhang <hzhang@stat.ncsu.edu> and Chen-Yen Lin
<clin5@ncsu.edu>
COSSO is the short term for Component Selection and
Smoothing Operator. It is a new regularization method for
multivariate function estimation and component. The COSSO
imposes a soft-thresholding penalty operator on function
components in the framework of reproducing kernel Hilbert space
(RKHS) to achieve sparsity. In a sense, the COSSO generalizes
the LASSO (Tibshirani 1996) for variable selection in linear
models to nonparametric regression models. Details about the
procedures are given in the papers by Lin and Zhang (2006) and
Zhang and Lin (2006).

COSSO is the short term for Component Selection and
Smoothing Operator. The new regularization method automatically
selects important function components by a soft-thresholding
penalty in the context of smoothing spline ANOVA models.  The
details about the procedures are given in the paper by Lin and
Zhang (2007).

COSSO, adaptive COSSO, variable selection for nonparametric
smoothing spline (quantile) regression models.