bases: Basis Expansions for Regression Modeling
Provides various basis expansions for flexible regression modeling,
including random Fourier features (Rahimi & Recht, 2007)
<https://proceedings.neurips.cc/paper_files/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf>,
exact kernel / Gaussian process feature maps, Bayesian Additive Regression
Trees (BART) (Chipman et al., 2010) <doi:10.1214/09-AOAS285> prior features,
and a helpful interface for n-way interactions. The provided functions may
be used within any modeling formula, allowing the use of kernel methods and
other basis expansions in modeling functions that do not otherwise support
them. Along with the basis expansions, a number of kernel functions are also
provided, which support kernel arithmetic to form new kernels. Basic ridge
regression functionality is included as well.
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