msir: Model-based Sliced Inverse Regression
An R package for dimension reduction based on Gaussian finite mixture models as an extension to sliced inverse regression (SIR) is available on CRAN.References:
- Scrucca L. (2011) Model-based SIR for dimension reduction. To appear in
Computational Statistics & Data Analysis.
Supplemental material.
Regularized Sliced Inverse Regression
The archive regsir.zip contains R functions to perform regularization and shrinkage for Sliced Inverse Regression (SIR) as described in- Scrucca L. (2006) Regularized sliced inverse regression with applications in classification (2006). In "Data Analysis, Classification and the Forward Search", editors Zani S., Cerioli A., Riani M., Vichi M., Berlin, Springer-Verlag, pp. 59-66.
- Scrucca L. (2007) Class prediction and gene selection for DNA microarrays using sliced inverse regression (2007). Computational Statistics & Data Analysis, Vol. 52, pp. 438-451.
qcc: Quality Control Charts
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. The 'qcc' package is available on CRAN.A short description of the package appeared in the newsletter R News, volume 4/1, June 2004, pp. 11-17.
Competing Risks Analysis
Functions (based on the cmprsk R package) and datasets for computing the cumulative incidence function in the presence of competing risks, testing equality across groups (Gray's test), and pointwise confidence intervals for competing risks curves. Reference:Scrucca L., Santucci A., Aversa F. (2007) Competing risks analysis using R: an easy guide for clinicians. Bone Marrow Transplantation, 40, 381-387.
Functions (based on the cmprsk R package) and dataset for regression modeling of competing risk. Reference:
Scrucca L., Santucci A., Aversa F. (2009) Regression modeling of competing risk using R: an in depth guide for clinicians. To appear in Bone Marrow Transplantation.