This package is part of eProsima FastDDS. RTPS is the wire interoperability protocol defined for the Data Distribution Service (DDS) standard by the Object Management Group (OMG).

R is a language and environment for statistical computing and graphics, providing a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, and so on).

Ansible is a radically simple model-driven configuration management, multi-node deployment, and remote task execution system. Ansible works over SSH and does not require any software or daemons to be installed on remote nodes. Extension modules can be written in any language and are transferred to managed machines automatically.

jnr-a64asm is a pure-java port of AsmJit, a lightweight library for machine code generation written in C++ language.

cpp-httplib is a C++11 cross platform HTTP/HTTPS library, with a focus on ease of use. This is a multi-threaded 'blocking' HTTP library. If you are looking for a 'non-blocking' library, this is not the one that you want.

Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias- reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi- complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).