This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

A toolbox for constructing potential landscapes for Ising
    networks. The parameters of the networks can be directly supplied by
    users or estimated by the 'IsingFit' package by van Borkulo and
    Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from
    empirical data. The Ising model's Boltzmann distribution is preserved
    for the potential landscape function. The landscape functions can be
    used for quantifying and visualizing the stability of network states,
    as well as visualizing the simulation process.

Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided.

Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.

Develops stochastic models based on the Theory of Island
    Biogeography (TIB) of MacArthur and Wilson (1967) <doi:10.1023/A:1016393430551>
    and extensions. It implements methods to estimate colonization and
    extinction rates (including environmental variables) given presence-absence
    data, simulates community assembly, and performs model selection.

An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.