Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions (MoTBFs)
Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks. The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called ‘motbf’ and ‘jointmotbf’.
Statistical Comparison of Multiple Algorithms in Multiple Problems (scmamp)
Given a matrix with results of different algorithms for different problems, the package uses statistical tests and corrections to assess the differences between algorithms.
Moving-Window Add-on for ‘plyr’ (rollply)
Apply a function in a moving window, then combine the results in a data frame.