Tools to run genome-wide association study (GWAS) and
  genome-wide by environment interaction study (GWEIS) scans using the genetic
  data stored in a binary dosage file. The user provides a data frame with
  the subject's covariate data and the information about the binary dosage
  file returned by the BinaryDosage::getbdinfo() routine.

OpenAI Gym is a open-source Python toolkit for developing and comparing
    reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API,
    and enables access to an ever-growing variety of environments.
    For more details on OpenAI Gym, please see here: <https://github.com/openai/gym>.
    For more details on the OpenAI Gym API specification, please see here:
    <https://github.com/openai/gym-http-api>.

Hyperbolic geometry in the Minkowski model and the Poincaré
    model. The methods are based on the gyrovector space theory developed
    by A. A. Ungar that can be found in the book 'Analytic Hyperbolic
    Geometry: Mathematical Foundations And Applications'
    <doi:10.1142/5914>. The package provides functions to plot
    three-dimensional hyperbolic polyhedra and to plot hyperbolic tilings
    of the Poincaré disk.

We provide a toolbox to conduct a Bayesian meta-analysis for estimating the current expansion rate of the Universe, called the Hubble constant H0, via time delay cosmography. The input data are Fermat potential difference and time delay estimates. For a robust inference, we assume a Student's t error for these inputs. Given these inputs, the meta-analysis produces posterior samples of the model parameters including the Hubble constant via Metropolis-Hastings within Gibbs. The package provides an option to implement repelling-attracting Metropolis-Hastings within Gibbs in a case where the parameter space has multiple modes.

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), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection,
    Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).