dfoptim: Derivative-Free Optimization

Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.

Version: 2018.2-1
Depends: R (≥ 2.10.1)
Published: 2018-04-02
Author: Ravi Varadhan, Johns Hopkins University, and Hans W. Borchers, ABB Corporate Research.
Maintainer: Ravi Varadhan <ravi.varadhan at jhu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.html
NeedsCompilation: no
Materials: NEWS
In views: Optimization
CRAN checks: dfoptim results


Reference manual: dfoptim.pdf
Package source: dfoptim_2018.2-1.tar.gz
Windows binaries: r-devel: dfoptim_2018.2-1.zip, r-release: dfoptim_2018.2-1.zip, r-oldrel: dfoptim_2018.2-1.zip
OS X binaries: r-release: dfoptim_2018.2-1.tgz, r-oldrel: dfoptim_2018.2-1.tgz
Old sources: dfoptim archive

Reverse dependencies:

Reverse depends: BivarP
Reverse imports: diffusion, DynTxRegime, matie, optimx, stepPenal
Reverse suggests: afex, metafor, SACOBRA


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