sparseHessianFD: Numerical Estimation of Sparse Hessians

Estimates Hessian of a scalar-valued function, and returns it in a sparse Matrix format. The sparsity pattern must be known in advance. The algorithm is especially efficient for hierarchical models with a large number of heterogeneous units. See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.

Version: 0.3.3.3
Depends: R (≥ 3.4.0)
Imports: Matrix (≥ 1.2.12), methods, Rcpp (≥ 0.12.13)
LinkingTo: Rcpp, RcppEigen (≥ 0.3.3.3.0)
Suggests: testthat, numDeriv, scales, knitr, xtable, dplyr
Published: 2018-03-27
Author: Michael Braun [aut, cre, cph]
Maintainer: Michael Braun <braunm at smu.edu>
License: MPL (== 2.0)
URL: http://www.smu.edu/Cox/Departments/FacultyDirectory/BraunMichael
NeedsCompilation: yes
SystemRequirements: C++11
Citation: sparseHessianFD citation info
Materials: NEWS
CRAN checks: sparseHessianFD results

Downloads:

Reference manual: sparseHessianFD.pdf
Vignettes: sparseHessianFD
Package source: sparseHessianFD_0.3.3.3.tar.gz
Windows binaries: r-prerel: sparseHessianFD_0.3.3.3.zip, r-release: sparseHessianFD_0.3.3.3.zip, r-oldrel: sparseHessianFD_0.3.3.zip
OS X binaries: r-prerel: sparseHessianFD_0.3.3.3.tgz, r-release: sparseHessianFD_0.3.3.3.tgz
Old sources: sparseHessianFD archive

Reverse dependencies:

Reverse suggests: bayesGDS

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