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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "RegressionFactory" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'RegressionFactory: Expander Functions for Generating Full Gradient and Hessian
  from Single-Slot and Multi-Slot Base Distributions'
version: 0.7.4
doi: 10.32614/CRAN.package.RegressionFactory
abstract: The expander functions rely on the mathematics developed for the Hessian-definiteness
  invariance theorem for linear projection transformations of variables, described
  in authors' paper, to generate the full, high-dimensional gradient and Hessian from
  the lower-dimensional derivative objects. This greatly relieves the computational
  burden of generating the regression-function derivatives, which in turn can be fed
  into any optimization routine that utilizes such derivatives. The theorem guarantees
  that Hessian definiteness is preserved, meaning that reasoning about this property
  can be performed in the low-dimensional space of the base distribution. This is
  often a much easier task than its equivalent in the full, high-dimensional space.
  Definiteness of Hessian can be useful in selecting optimization/sampling algorithms
  such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton
  Sampler. Finally, in addition to being a computational tool, the regression expansion
  framework is of conceptual value by offering new opportunities to generate novel
  regression problems.
authors:
- family-names: Mahani
  given-names: Alireza S.
  email: alireza.s.mahani@gmail.com
- family-names: Mahani
  given-names: Alireza S.
- family-names: Sharabiani
  given-names: Mansour T.A.
repository: https://asmahani.r-universe.dev
commit: 25f2fcc63af6a1ffec6fd5d4e7ed888aa1c291b6
date-released: '2020-10-26'
contact:
- family-names: Mahani
  given-names: Alireza S.
  email: alireza.s.mahani@gmail.com