A new version 0.2.0 of the RcppSMC package arrived on CRAN earlier today (as a very quick pretest-publish within minutes of submission).

RcppSMC provides Rcpp-based bindings to R for the Sequential Monte Carlo Template Classes (SMCTC) by Adam Johansen described in his JSS article.

This release 0.2.0 is chiefly the work of Leah South, a Ph.D. student at Queensland University of Technology, who was during the last few months a Google Summer of Code student mentored by Adam and myself. It was pleasure to work with Leah on this, and see her progress. Our congratulations to Leah for a job well done!

Changes in RcppSMC version 0.2.0 (2017-08-28)

• Also use .registration=TRUE in useDynLib in NAMESPACE

• Multiple Sequential Monte Carlo extensions (Leah South as part of Google Summer of Code 2017)

  • Switching to population level objects (#2 and #3).

  • Using Rcpp attributes (#2).

  • Using automatic RNGscope (#4 and #5).

  • Adding multiple normalising constant estimators (#7).

  • Static Bayesian model example: linear regression (#10 addressing #9).

  • Adding a PMMH example (#13 addressing #11).

  • Framework for additional algorithm parameters and adaptation (#19 addressing #16; also #24 addressing #23).

  • Common adaptation methods for static Bayesian models (#20 addressing #17).

  • Supporting MCMC repeated runs (#21).

  • Adding adaptation to linear regression example (#22 addressing #18).

Courtesy of CRANberries, there is a diffstat report for this release.

More information is on the RcppSMC page. Issues and bugreports should go to the GitHub issue tracker.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.