Title: NNG (Nanomsg Next Gen) Lightweight Messaging Library
Description: R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. NNG is
a socket library providing high-performance scalability protocols, or common
communication patterns, the basic building blocks for distributed systems.

a socket library providing high-performance scalability protocols,
implementing a cross-platform standard for messaging and communications.
Serves as a concurrency framework that can be used for building distributed
SystemRequirements: either 'libnng' (deb: libnng-dev, rpm: nng-devel)

Serves as a concurrency framework for building distributed applications,
utilising 'Aio' objects which automatically resolve upon completion of

SystemRequirements: 'cmake' to compile 'libnng' from source

SystemRequirements: 'cmake' to compile 'libnng' and 'libmbedtls' from

SystemRequirements: 'libnng' >= 1.6.0 and 'libmbedtls' >= 2, or 'cmake'
to compile NNG and/or Mbed TLS included in package sources

utilising 'aio' objects which resolve automatically upon completion of

Title: tools for nanoparticle simulation and PDF calculation
Description: This software package implements functions to simulate
spherical monoatomic nanoparticles with face-centered cubic
structure and calculate the associated pair distribution and

Title: Tools for Nanoparticle Simulation and Calculation of PDF and
Description: This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structure and calculate the associated pair distribution and total scattering structure functions.

Description: This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to calculate the associated pair-distribution function and X-ray/neutron total-scattering signals. It also provides a target function that can be used for simultaneous fitting of small- and wide-angle total scattering data in real and reciprocal spaces. The target function can be generated either as a sum of weighted residuals for individual datasets or as a vector of residuals suitable for optimization using multi-criteria algorithms (e.g. Pareto methods).