Fractional polynomials ("FPs") are an automatic way of fitting non-linear, parametric effects. The R-package mfp implements a frequentist inference approach for FP models. Recently, we have proposed a Bayesian inference approach for normal FP models, which is based on the quasi-default hyper-/g/ prior for the regression coefficients . This approach is implemented in the new R-package "bfp".
The R-package bfp (current version: 0.0-17) is now available on CRAN . The current development version is (still) available on R-Forge .
For a quick start try:
and if you have more time, to reproduce results from the paper :
Questions, suggestions or critique are very welcome!
 Statistics & Computing paper:
 CRAN: http://cran.r-project.org/web/packages/bfp/index.html
 R-Forge: http://r-forge.r-project.org/projects/bfp/
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