A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).

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.