Description: Contains data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples by Shumway and Stoffer, fourth edition, <http://www.stat.pitt.edu/stoffer/tsa4/>.

Description: Included are data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples (4th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017. <DOI:10.1007/978-3-319-52452-8>.

Description: Functions and data sets for a lecture in Advanced Statistics using R. Especially the functions mancontr() and inspect() may be of general interest. With mancontr(), manual contrasts, it is easily possible to specify your own contrasts and give them useful names. Something that is important in most projects of reasonable size. The function inspect() allows you to plot a wide range of inspection plots to validate model assumptions (currently for lm and glm, but it is constantly improved). And do not forget to have a look at norm.test(), it is not only fun!
Packaged: Thu Mar 29 22:13:28 2007; thomasfabbro

Description: Functions and data sets for a lecture in Advanced Statistics using R. Especially the functions mancontr() and inspect() may be of general interest. With mancontr(), short for manual contrasts, it is possible to specify your own contrasts and give them useful names. Something that is important in most projects of reasonable size. The function inspect() shows a wide range of inspection plots to validate model assumptions (currently for models fitted with lm, glm, and lmer). And do not forget to have a look at norm.test(), it is not only fun!
Depends: MASS, lattice, grid, methods, lme4
Packaged: Fri Jun  1 16:13:13 2007; thomasfabbro

Title: Adaptive SURE Thresholding Using Side Information
Description: Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity.
It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data.
For more information, please see the website <http://www-bcf.usc.edu/~wenguans/Papers/ASUS.htm> and the accompanying paper.