Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method that can be used to fit multidimensional functions for regression and classification problems without relying on linearity or additivity assumptions. This package is currently in alpha phase (feedback is appreciated).

Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions for regression and classification problems without relying on linearity or additivity assumptions. This package is currently in alpha phase (feedback is appreciated).

Kernel-based Regularized Least squares (KRLS)
Jens Hainmueller (Stanford) Chad Hazlett (MIT)
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).

Jens Hainmueller (Stanford) Chad Hazlett (UCLA)

person("Laval", "Jacquin", email = "jacquin.julien@gmail.com", role = c("aut", "cre"))
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.

Retained Component Criterion for Principal Component Analysis
The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.