H323Plus 1.27.2 and PTLib 2.10.9.4 have been released.

Changes in H323Plus:

- support for Alpine Linux for smaller container images
- crash fixed on invalid RTCP packets
- memory leaks fixed
- GetCrytoMasterKey() restored that got lost in 1.27.1
- better support for cross-compiling
- various updates for newer compilers
- some smaller bug fixes

https://www.h323plus.org/source/

Changes in PTLib:

- support for Alpine Linux
- better support for cross-compiling
- various smaller bug fixes

https://github.com/willamowius/ptlib/releases

I have just bundeled up the changes and bug fixes of the past 2 years and released PTLib 2.10.9.6.

Most notable in this release is working IPv6 on *BSD, macOSX and Solaris as well as support for newer compilers and many small platform fixes.

Since PTLib is the foundation for the GNU Gatekeeper and many H323Plus projects, all these improvements get propagated into those projects as well.

Changes:
- IPv6 support fixed for *BSD, macOSX and Solaris
- support for newer compiler, eg. gcc 13 and VS2022
- support for C++-17
- support for Win64 builds
- support AIX as platform
- small OpenBSD fixes
- other small fixes

Download from https://www.h323plus.org/source/

Manifold optimization appears in a wide variety of computational problems in the applied sciences. In recent statistical methodologies such as sufficient dimension reduction and regression envelopes, estimation relies on the optimization of likelihood functions over spaces of matrices such as the Stiefel or Grassmann manifolds. Recently, Huang, Absil, Gallivan, and Hand (2016) have introduced the library ROPTLIB, which provides a framework and state of the art algorithms to optimize real-valued objective functions over commonly used matrix-valued Riemannian manifolds. This article presents ManifoldOptim, an R package that wraps the C++ library ROPTLIB. ManifoldOptim enables users to access functionality in ROPTLIB through R so that optimization problems can easily be constructed, solved, and integrated into larger R codes. Computationally intensive problems can be programmed with Rcpp and RcppArmadillo, and otherwise accessed through R. We illustrate the practical use of ManifoldOptim through several motivating examples involving dimension reduction and envelope methods in regression.