Implements Kernelbased 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).
Package details 


Author  Jens Hainmueller (Stanford) Chad Hazlett (UCLA) Luke Sonnet (UCLA) 
Maintainer  Jens Hainmueller <[email protected]> 
License  GPL (>= 2) 
Version  1.1.0 
URL  https://www.rproject.org https://www.stanford.edu/~jhain/ 
Package repository  View on GitHub 
Installation 
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