fdskrls

Object from call to <code><a rd-options="" href="/link/krls?package=KRLS&version=1.0-0" data-mini-rdoc="KRLS::krls">krls</a></code>.

object

additional arguments to be passed to lower level functions

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Internal function that is called by <code><a rd-options="" href="/link/krls?package=KRLS&version=1.0-0" data-mini-rdoc="KRLS::krls">krls</a></code> to computes first differences for binary predictors in the X matrix. It would normally not be called by the user directly.

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

KRLS

Kernel-Based Regularized Least Squares

fdskrls function

Object from call to <code><a rd-options='' href='krls'>krls</a></code>.

Internal function that is called by <code><a rd-options='' href='krls'>krls</a></code> to computes first differences for binary predictors in the X matrix. It would normally not be called by the user directly.

fdskrls: Compute first differences with KRLS

Description

Usage

Arguments

Value

See Also