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).
|Author||Jens Hainmueller (Stanford) Chad Hazlett (UCLA)|
|Date of publication||2014-05-21 21:21:47|
|Maintainer||Jens Hainmueller <email@example.com>|
|License||GPL (>= 2)|
fdskrls: Compute first differences with KRLS
gausskernel: Gaussian Kernel Distance Computation
krls: Kernel-based Regularized Least Squares (KRLS)
lambdasearch: Leave-one-out optimization to find lambda
looloss: Loss Function for Leave One Out Error
plot.krls: Plot method for Kernel-based Regularized Least Squares (KRLS)...
predict.krls: Predict method for Kernel-based Regularized Least Squares...
solveforc: Solve for Choice Coefficients in KRLS
summary.krls: Summary method for Kernel-based Regularized Least Squares...
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