Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).

A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.

Distributes Gaussian process calculations across nodes
    in a distributed memory setting, using Rmpi. The bigGP class 
    provides high-level methods for maximum likelihood with normal data, 
    prediction, calculation of uncertainty (i.e., posterior covariance 
    calculations), and simulation of realizations. In addition, bigGP 
    provides an API for basic matrix calculations with distributed 
    covariance matrices, including Cholesky decomposition, back/forwardsolve, 
    crossproduct, and matrix multiplication.

Response surface methods for drug synergy analysis. Available
    methods include generalized and classical Loewe formulations as well as Highest
    Single Agent methodology. Response surfaces can be plotted in an interactive
    3-D plot and formal statistical tests for presence of synergistic effects are
    available. Implemented methods and tests are described in the article 
    "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction 
    of the expected combined effect compatible with partial agonism and antagonism"
    by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, 
    Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) 
    <doi:10.1038/s41598-017-18068-5>.