Matrix, Rcpp, nloptr, igraph, grid, gridExtra, dplyr, tidyr,
purrr, ggplot2, corrplot, magrittr, rlang

person("Giovanni", "Poggiato", role = "ctb", email = "giov.poggiato@gmail.com"),

person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inra.fr",
person("Mahendra", "Mariadassou", role = "aut", email = "mahendra.mariadassou@inra.fr",
person("Stéphane", "Robin", role ="ctb", email = "stephane.robin@inra.fr")
network inference (Chiquet, Mariadassou and Robin, 2018 <arXiv:1806.03120>).
ggplot2, corrplot, magrittr, biomformat, phyloseq
Suggests: knitr, rmarkdown, GGally, testthat, covr, pkgdown

Suggests: knitr, rmarkdown, testthat, covr, pkgdown

Title: Prediction with Less Overfitting and Robust to Noise
Author: Takahiko Koizumi, Kenta Suzuki, Yasunori Ichihashi
Description: A method for the quantitative prediction with much predictors.
This package provides functions to construct the quantitative prediction
model with less overfitting and robust to noise.

Package: PLRModels
Type: Package
Title: Statistical inference in partial linear regression models
Version: 1.0
Date: 2013-06-26
Author: German Aneiros Perez and Ana Lopez Cheda
Maintainer: German Aneiros Perez <ganeiros@udc.es>
Description: This package provides statistical inference tools applied
to Partial Linear Regression (PLR) models. Specifically, point
estimation, bandwidth selection, goodness-of-fit tests and
analysis of covariance are considered.  Kernel-based methods,
combined with ordinary least squares estimation, are used and
time series errors are allowed. In addition, these techniques
are also implemented for both parametric (linear) and
nonparametric regression models.
License: GPL
Packaged: 2013-06-26 11:11:58 UTC; ana
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-06-26 18:08:52

Description: This package provides statistical inference tools applied to Partial Linear Regression (PLR)
models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of
Kernel-based methods, combined with ordinary least squares estimation, are used and time series
errors are allowed. In addition, these techniques are also implemented for both parametric (linear)