Commit 7c7268b0 authored by Tom Reynkens's avatar Tom Reynkens

Update Devriendt et al. (2018) reference

parent b13121a3
Type: Package
Package: smurf
Title: Sparse Multi-Type Regularized Feature Modeling
Version: 0.4.1.9005
Version: 0.4.1.9006
Date: 2018-10-09
Authors@R: c(
person("Tom", "Reynkens", email = "tomreynkens@hotmail.com", role = c("aut", "cre"),
......
......@@ -78,7 +78,7 @@
#'
#' @seealso \code{\link{glmsmurf-class}}, \code{\link{glmsmurf.control}}, \code{\link{p}}, \code{\link[stats]{glm}}
#'
#' @references Devriendt, S., Antonio, K., Reynkens, T. and Verbelen, R. (2018). "Sparse Multi-type Regularized Feature Modeling in Regression".
#' @references Devriendt, S., Antonio, K., Reynkens, T. and Verbelen, R. (2018). "Sparse Regression with Multi-type Regularized Feature Modeling." \emph{arXiv:1810.03136}.
#'
#' Hastie, T., Tibshirani, R., and Wainwright, M. (2015). \emph{Statistical Learning with Sparsity: The Lasso and Generalizations}. CRC Press.
#' @example /inst/Rent_example1.R
......
......@@ -22,7 +22,7 @@ knitr::opts_chunk$set(
## Overview
*smurf* contains the implementation of the SMuRF (Sparse Multi-type Regularized Feature modeling) algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:
*smurf* contains the implementation of the SMuRF (Sparse Multi-type Regularized Feature modeling; see Devriendt et al., 2018) algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:
* Selection of the regularization tuning parameter `lambda` using three different approaches: in-sample, out-of-sample or using cross-validation.
* S3 methods to handle the fitted object including visualization of the coefficients and a model summary.
......@@ -42,4 +42,8 @@ devtools::install_git("https://gitlab.com/TReynkens/smurf.git")
```
## Authors
Tom Reynkens, Sander Devriendt and Katrien Antonio
\ No newline at end of file
Tom Reynkens, Sander Devriendt and Katrien Antonio
## Reference
Devriendt, S., Antonio, K., Reynkens, T. and Verbelen, R. (2018). "Sparse Regression with Multi-type Regularized Feature Modeling." *arXiv:1810.03136*.
\ No newline at end of file
......@@ -10,10 +10,10 @@ report](https://gitlab.com/TReynkens/smurf/badges/master/coverage.svg)](https://
## Overview
*smurf* contains the implementation of the SMuRF (Sparse Multi-type
Regularized Feature modeling) algorithm to fit generalized linear models
(GLMs) with multiple types of predictors via regularized maximum
likelihood. Next to the fitting procedure, following functionality is
available:
Regularized Feature modeling; see Devriendt et al., 2018) algorithm to
fit generalized linear models (GLMs) with multiple types of predictors
via regularized maximum likelihood. Next to the fitting procedure,
following functionality is available:
- Selection of the regularization tuning parameter `lambda` using
three different approaches: in-sample, out-of-sample or using
......@@ -42,3 +42,9 @@ Then, install the latest development version of *smurf* using
## Authors
Tom Reynkens, Sander Devriendt and Katrien Antonio
## Reference
Devriendt, S., Antonio, K., Reynkens, T. and Verbelen, R. (2018).
“Sparse Regression with Multi-type Regularized Feature Modeling.”
*arXiv:1810.03136*.
......@@ -14,6 +14,7 @@
\itemize{
\item Remove maintainer field in DESCRIPTION as it is already set using Authors@R.
\item Change GitLab URL in README.
\item Update Devriendt et al. (2018) reference.
}
}
......
......@@ -194,7 +194,7 @@ plot_reest(munich.fit)
summary(munich.fit)
}
\references{
Devriendt, S., Antonio, K., Reynkens, T. and Verbelen, R. (2018). "Sparse Multi-type Regularized Feature Modeling in Regression".
Devriendt, S., Antonio, K., Reynkens, T. and Verbelen, R. (2018). "Sparse Regression with Multi-type Regularized Feature Modeling." \emph{arXiv:1810.03136}.
Hastie, T., Tibshirani, R., and Wainwright, M. (2015). \emph{Statistical Learning with Sparsity: The Lasso and Generalizations}. CRC Press.
}
......
......@@ -37,7 +37,8 @@
@ARTICLE{SMuRF,
author = {Devriendt, S. and Antonio, K. and Reynkens, T. and Verbelen, R.},
title = {Sparse Multi-type Regularized Feature Modeling in Regression},
title = {Sparse Regression with Multi-type Regularized Feature Modeling},
journal = {arXiv:1810.03136},
year = {2018}
}
......
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