Description: Classical Ising Model is a land mark system in statistical physics. Model explains physics of spin glasses and magnetic materials, but used in many different context for example phase transitions and neural systems. This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions.

Title: Monte Carlo for Classical Ising Model

Description: 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.

Title: Stochastic Island Biogeography Theory Made Easy
person("David","Alonso", email = "dalonso@ceab.csic.es", role = "aut"))
Description: Tools to develop stochastic models based on the Theory of Island
Biogeography (TIB) of MacArthur and Wilson (1967) <DOI:10.1023/A:1016393430551>
and extensions. The package implements methods to estimate colonization and
extinction rates (including environmental variables) given presence-absence
data, simulate community assembly, and perform model selection.
Packaged: 2016-04-06 16:00:38 UTC; usercluster

Suggests: testthat, knitr, rmarkdown, rootSolve

Description: 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
data, simulates community assembly, and performs model selection.

Description: An implementation of the induced smoothing idea that focuses on hypothesis testing in lasso regularization models (IS-lasso). Linear, logistic, Poisson and gamma regressions with several link functions are already implemented. The algorithm uses the steps as described in the original paper. See: The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression. Cilluffo, G., Sottile, G., La Grutta, S. and Muggeo, V. (2019) <doi:10.1177/0962280219842890>.
Packaged: 2019-04-28 09:04:42 UTC; gianlucasottile