James Yang and I are thrilled to announce the new CRAN package RcppFastAD which arrived at CRAN last Monday as version 0.0.1, and is as of today at version 0.0.2 with a first set of small updates.
It is based on the FastAD header-only C++
library by James which provides a C++ implementation of both forward and
reverse mode of automatic differentiation in an easy-to-use header
library (which we wrapped here) that is both lightweight and performant.
With a little of bit of Rcpp glue, it
is also easy to use from R in simple C++ applications. Included in the
package are three example: a simple quadratic expression evaluating
x' S x for given x and S return the expression value with a
gradient, a linear regression example generalising this and using the
gradient to derive to arrive at the least-squares minimizing solution,
as well as the well-known Black-Scholes options pricer and its important
partial derivatives delta, rho, theta and vega derived via automatic
differentiation.
The NEWS file for these two initial releases follows.
Changes in version 0.0.2 (2023-03-05)
One C++ operation is protected from operating on a
nullptrAdditional tests have been added, tests now cover all three demo / example functions
Return values and code for the examples
linear_regressionandquadratic_expressionhave been adjustedChanges in version 0.0.1 (2023-02-24)
- Initial release version and CRAN upload
Courtesy of my CRANberries, there is also a diffstat report for the most recent release. More information is available at the repository or the package page.
If you like this or other open-source work I do, you can now sponsor me at GitHub.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.