knitr, rmarkdown, testthat, covr, pkgdown

knitr, rmarkdown, testthat, covr, pkgdown, biomformat,
phyloseq

Prediction with Less Overfitting and Robust to Noise
Takahiko Koizumi, Kenta Suzuki, Yasunori Ichihashi
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.

Statistical inference in partial linear regression models
German Aneiros Perez and Ana Lopez Cheda
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.

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
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.

Statistical Inference in Partial Linear Regression Models
German Aneiros Perez and Ana Lopez-Cheda
Contains 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 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.

Principal Component Analysis (PCA) biplots, Covariance monoplots
and biplots, Partial Least Squares (PLS) biplots, Partial Least Squares for
Generalized Linear Model (PLS-GLM) biplots, Sparse Partial Least Squares
(SPLS) biplots and Sparse Partial Least Squares for Generalized Linear
Model (SPLS-GLM) biplots.
c(person("Opeoluwa F.","Oyedele", role = c("aut", "cre"), email =
"OpeoluwaOyedele@gmail.com"), person("Sugnet", "Gardner-Lubbe", role =
"aut"))
chemometrics, MASS, mixOmics, mvabund, pls, plsgenomics,
robustbase, SensoMineR
Opeoluwa F. Oyedele [aut, cre],
Sugnet Gardner-Lubbe [aut]