np

Nonparametric Kernel Smoothing Methods for Mixed Data Types

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Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, https://www.nserc-crsng.gc.ca/), the Social Sciences and Humanities Research Council of Canada (SSHRC, https://www.sshrc-crsh.gc.ca/), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, https://sharcnet.ca/). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.


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