3 packages on CRAN
R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Provides a full implementation of the 'Jupyter' <http://jupyter.org/> messaging protocol in C++ by leveraging 'Rcpp' and 'Xeus' <https://github.com/QuantStack/xeus>. 'Jupyter' supplies an interactive computing environment and a messaging protocol defined over 'ZeroMQ' for multiple programming languages. This package implements the 'Jupyter' kernel interface so that 'R' is exposed to this interactive computing environment. 'ZeroMQ' functionality is provided by the 'pbdZMQ' package. 'Xeus' is a C++ library that facilitates the implementation of kernels for 'Jupyter'. Additionally, 'Xeus' provides an interface to libraries that exist in the 'Jupyter' ecosystem for building widgets, plotting, and more <https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92>. 'JuniperKernel' uses 'Xeus' as a library for the 'Jupyter' messaging protocol.
'ZeroMQ' is a well-known library for high-performance asynchronous messaging in scalable, distributed applications. This package provides high level R wrapper functions to easily utilize 'ZeroMQ'. We mainly focus on interactive client/server programming frameworks. For convenience, a minimal 'ZeroMQ' library (4.2.2) is shipped with 'pbdZMQ', which can be used if no system installation of 'ZeroMQ' is available. A few wrapper functions compatible with 'rzmq' are also provided.