k-Nearest Neighbor Mutual Information Estimator

This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) , BC Ross (2014), and A. Tsimpiris (2012) , respectively.

Parameter Estimation for the Averaging Model of Information Integration Theory

Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) < https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.

Physics-Informed Spatial and Functional Data Analysis

An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.

Life History Metrics from Matrix Population Models

Functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) .