Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jeremy Ferwerda, Jens Hainmueller, Chad J. Hazlett
Title: Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)
Abstract: The Stata package krls as well as the R package KRLS implement kernel-based regularized least squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2014) that allows users to tackle regression and classification problems without strong functional form assumptions or a specification search. The flexible KRLS estimator learns the functional form from the data, thereby protecting inferences against misspecification bias. Yet it nevertheless allows for interpretability and inference in ways similar to ordinary regression models. In particular, KRLS provides closed-form estimates for the predicted values, variances, and the pointwise partial derivatives that characterize the marginal effects of each independent variable at each data point in the covariate space. The method is thus a convenient and powerful alternative to ordinary least squares and other generalized linear models for regression-based analyses.

Page views:: 2633. Submitted: 2013-10-09. Published: 2017-07-13.
Paper: Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)     Download PDF (Downloads: 3119)
Supplements: Stata source package Download (Downloads: 117; 12KB) Stata replication code Download (Downloads: 172; 5KB)
KRLS_1.0-0.tar.gz: R source package Download (Downloads: 137; 36KB)
v79i03.R: R replication code Download (Downloads: 167; 5KB)
growthdata.dta: Supplementary data (Stata binary format) Download (Downloads: 176; 3KB)

DOI: 10.18637/jss.v079.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.