A method for the Bayesian functional linear regression model (scalar-on-function),
including two estimators of the coefficient function and an estimator of its support.
A representation of the posterior distribution is also available. Grollemund P-M., Abraham C.,
Baragatti M., Pudlo P. (2019)
Bayesian functional Linear regression with Sparse Step functions (BLiSS)
A method for the Bayesian Functional Linear Regression model (functions-on-scalar), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available.
Package In progress...
To install the bliss package, the easiest is to install it directly from GitHub. Open an R session and run the following commands:
Once the package is installed on your computer, it can be loaded into a R session:
As a lot of time and effort were spent in creating the bliss method, please cite it when using it for data analysis:
Grollemund, Paul-Marie; Abraham, Christophe; Baragatti, Meïli; Pudlo, Pierre. Bayesian Functional Linear Regression with Sparse Step Functions. Bayesian Anal. 14 (2019), no. 1, 111--135. doi:10.1214/18-BA1095. https://projecteuclid.org/euclid.ba/1524103229
You should also cite the bliss package:
See also citation() for citing R itself.