An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers. BARS (DiMatteo, Genovese, and Kass 2001) uses the powerful reversible-jump MCMC engine to perform spline-based generalized nonparametric regression. It has been shown to work well in terms of having small mean-squared error in many examples (smaller than known competitors), as well as producing visually-appealing fits that are smooth (filtering out high-frequency noise) while adapting to sudden changes (retaining high-frequency signal). However, BARS is computationally intensive. The original implementation in S was too slow to be practical in certain situations, and was found to handle some data sets incorrectly. We have implemented BARS in C for the normal and Poisson cases, the latter being important in neurophysiological and other point-process applications. The C implementation includes all needed subroutines for fitting Poisson regression, manipulating B-splines (using code created by Bates and Venables), and finding starting values for Poisson regression (using code for density estimation created by Kooperberg). The code utilizes only freely-available external libraries (LAPACK and BLAS) and is otherwise self-contained. We have also provided wrappers so that BARS can be used easily within S or R.

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 4 articles , 1 standard article )

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  1. Hartman, Brian M.; Mallick, Bani K.; Talukdar, Debabrata: Investigating international new product diffusion speed: A semiparametric approach (2012)
  2. Smith, Anne C.; Scalon, Joao D.; Wirth, Sylvia; Yanike, Marianna; Suzuki, Wendy A.; Brown, Emery N.: State-space algorithms for estimating spike rate functions (2010) ioport
  3. Scheipl, Fabian; Kneib, Thomas: Locally adaptive Bayesian P-splines with a normal-exponential-gamma prior (2009)
  4. Garrick Wallstrom; Jeffrey Liebner; Robert Kass: An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers (2008) not zbMATH