Kevin Kuo
10 packages on CRAN
Helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R interface for 'GraphFrames' <https://graphframes.github.io/>. 'GraphFrames' is a package for 'Apache Spark' that provides a DataFrame-based API for working with graphs. Functionality includes motif finding and common graph algorithms, such as PageRank and Breadth-first search.
A 'sparklyr' <https://spark.rstudio.com> extension that provides an interface to 'MLeap' <https://github.com/combust/mleap>, an open source library that enables exporting and serving of 'Apache Spark' pipelines.
A 'sparklyr' extension that enables reading and writing 'TensorFlow' TFRecord files via 'Apache Spark'.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 'XGBoost' is an optimized distributed gradient boosting library.
Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/programmers_guide/estimators>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.
R interface to 'MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models.
R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.