Title: Statistical Inference in Partial Linear Regression Models
Author: German Aneiros Perez and Ana Lopez-Cheda
Description: 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.

Title: The Partial Least Squares (PLS) Biplot
Description: 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
Authors@R: c(person("Opeoluwa F.","Oyedele", role = c("aut", "cre"), email =
"OpeoluwaOyedele@gmail.com"), person("Sugnet", "Gardner-Lubbe", role =
Maintainer: Opeoluwa F. Oyedele <OpeoluwaOyedele@gmail.com>
Suggests: chemometrics, MASS, mixOmics, mvabund, pls, plsgenomics,

Title: Methods for Partial Linear Single Index Model
Author: Shunyao Wu, Qi Zhang, Zhiruo Li, Hua Liang
Description: Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at <doi:10.1214/10-AOS835> for more details.
Imports: Rcpp (>= 0.12.11), crayon, methods, stats

Imports: Rcpp (>= 0.12.11), crayon, methods, stats, purrr

Title: Estimate (Generalized) Linear Mixed Models with Factor
Authors@R: c(person("Minjeong", "Jeon", role = "aut"),
person("Nicholas", "Rockwood", email = "rockwood.19@osu.edu", role = c("aut", "cre")))
Description: Utilizes the 'lme4' package and the optim() function from 'stats'
to estimate (generalized) linear mixed models (GLMM) with factor
structures using a profile likelihood approach, as outlined in
Factor analysis and item response models can be extended to allow
for an arbitrary number of nested and crossed random effects,
making it useful for multilevel and cross-classified models.
Imports: lme4, Matrix (>= 1.1.1), numDeriv, stats
Packaged: 2017-07-18 19:42:03 UTC; nickrockwood