Bayesian Functional Linear Regression with Sparse Step Functions

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 et. al. (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...

Installation

To install the bliss package, the easiest is to install it directly from GitHub. Open an R session and run the following commands:

library(remotes) 
install_github("pmgrollemund/bliss", build_vignettes=TRUE)

Usage

Once the package is installed on your computer, it can be loaded into a R session:

library(bliss)
help(package="bliss")

Citation

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:

citation("bliss")

See also citation() for citing R itself.

News

Reference manual

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install.packages("bliss")

1.0.0 by Paul-Marie Grollemund, 4 months ago


Browse source code at https://github.com/cran/bliss


Authors: Paul-Marie Grollemund [aut, cre] , Isabelle Sanchez [ctr] , Meili Baragatti [ctr]


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, MASS, RColorBrewer, ggplot2, rockchalk

Suggests rmarkdown, knitr

Linking to Rcpp, RcppArmadillo


See at CRAN