bsamGP: R-Package for Bayesian Spectral Analysis Models using Gaussian Process Priors
by Seongil Jo, Taeryon Choi, Beomjo Park, and Peter Lenk,  August. 2017.
This R-package complements the paper Jo, S., Choi, T., Park, B., & Lenk, P. (2017) "bsamGP: An R package for Bayesian Spectral Analysis Models using Gaussian Process Priors", Preprint

bsamGP implements Bayesian Spectral Analysis Models that include regression with/without shape restrictions [1], quantile regression with/without shape restrictions [2], additive models, generalized linear models and density estimation [3]. You may download R-package compiled for Windows(link) and for Mac(link). The package is also availabe in CRAN(link)To install package, use RGui menu "Package/Install package(s) from local zip files..." or R command "install.packages". For detailed description for the package, check this manual and the paper.

We provide a simple example. To carry out examples, install bsamGP into R and run following example R-code.

Bayesian Spectral Analysis Regression (BSAR)
  • Example for mean regression of function with monotone increasing and convex constraint.
Bayesian Spectral Analysis Quantile Regression (BSAQ)
  • Example for median regression of function with monotone convex to concave increasing constraint
Bayesian Spectral Analysis Generalized Linear Regression (GBSAR)
  • Example for probit regression of function with monotone increasing and convex constraint
Bayesian Spectral Analysis Density Estimation (BSAD)
  • Example for semiparametric density estimation with truncated gamma distribution and sigmoid density.

[1] Lenk P, Choi T (2017).“Bayesian analysis of shape-restricted functions using Gaussian process
priors.” Stat Sinica, 27(1), 43–69.
[2] Jo S, Roh T, Choi T (2016). “Bayesian spectral analysis models for quantile regression with
Dirichlet process mixtures.” J. Nonparametr. Stat., 28(1), 177–206.
[3] Lenk P (2003). “Bayesian semiparametric density estimation and model verification using a
logistic-Gaussian process.” J. Comput. Graph. Stat., 12(3), 548–565