Package: sparseHessianFD
Type: Package
Title: Numerical Estimation of Sparse Hessians
Version: 0.2.0
Date: 2015-01-29
Author: R interface code by Michael Braun
    Original Fortran code by Thomas F. Coleman, Burton S. Garbow and
    Jorge J. More.
Maintainer: Michael Braun <braunm@smu.edu>
URL: coxprofs.cox.smu.edu/braunm
Description: Computes Hessian of a scalar-valued function, and returns it in
    sparse Matrix format, using ACM TOMS Algorithm 636. The user must supply the objective function, the
    gradient, and the row and column indices of the non-zero elements of the
    lower triangle of the Hessian (i.e., the sparsity structure must be known
    in advance). The algorithm exploits this sparsity, so Hessians can be
    computed quickly even when the number of arguments to the objective
    functions is large. This package is intended to be useful for numeric
    optimization (e.g., with the trustOptim package) or in simulation (e.g.,
    the sparseMVN package). The underlying algorithm is ACM TOMS Algorithm 636,
    written by Coleman, Garbow and More (ACM Transactions on Mathematical
    Software, 11:4, Dec. 1985).
License: file LICENSE
LazyData: true
Depends: R (>= 3.1.2), Rcpp (>= 0.11.3), Matrix (>= 1.1.4), methods
Suggests: testthat, numDeriv, knitr
RcppModules: sparseHessianFD
LinkingTo: Rcpp, RcppEigen (>= 0.3.2.3)
VignetteBuilder: knitr
SystemRequirements: C++11
Packaged: 2015-02-04 17:50:55 UTC; braunm
NeedsCompilation: yes
License_restricts_use: yes
Repository: CRAN
Date/Publication: 2015-02-04 19:14:05
