aws

Adaptive weights smoothing

AWS is a contributed package within the R-Project for Statistical Computing containing a reference implementation of the adaptive weights smoothing algorithms for local constant likelihood and local polynomial regression models. Binaries for several operating systems are available from the Comprehensive R Archive Network (http://cran.r-project.org).

Authors: Joerg Polzehl

Reference, including documentation:
J. Polzehl, V.Spokoiny (2006). Propagation-separation approach for local likelihood estimation, Probab. Theory Related Fields, 135 pp. 335--362.
J. Polzehl, K. Papafitsoros, K. Tabelow (2020). Patch-wise adaptive weights smoothing in R, Journal of Statistical Software, 95, pp. 1--27.
J. Polzehl and K. Tabelow (2023). Magnetic Resonance Brain Imaging: Modeling and Data Analysis with R 2nd Revised Edition, Series: Use R!, Springer International Publishing, Cham, 2023, 258 pages.

Download: freely available from CRAN server under GPL.

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Last modified: Tue Mar 18 16:00:00 CET 2024