sparseHessianFD: Numerical estimation of sparse Hessians using ACM TOMS Algorithm 636

Computes Hessian of a scalar-valued function, and returns it in sparse Matrix format. 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).

Version: 0.1.1
Depends: Rcpp (≥ 0.9.6), RcppEigen (≥ 0.3.1), Matrix, methods
LinkingTo: Rcpp, RcppEigen
Suggests: plyr
Published: 2013-11-06
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 at>
License: file LICENSE
NeedsCompilation: yes
Materials: NEWS
CRAN checks: sparseHessianFD results


Reference manual: sparseHessianFD.pdf
Vignettes: Using the sparseHessianFD package
Package source: sparseHessianFD_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: sparseHessianFD_0.1.1.tgz, r-oldrel: sparseHessianFD_0.1.1.tgz
OS X Mavericks binaries: r-release: sparseHessianFD_0.1.1.tgz
Old sources: sparseHessianFD archive

Reverse dependencies:

Reverse suggests: bayesGDS, trustOptim