Network structural equation modeling (netSEM) is a data-driven modeling technique that has been developed at the SDLE Research Center and with a public version available as a R software package CRAN. netSEM selects the best relationship between variables based on statistical significance such as adjusted R2 and also rank order models using Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC).
BayesO (pronounced “bayes-o”) is a simple, but essential Bayesian optimization package, written in Python. It is designed to run advanced Bayesian optimization with implementation specific and application-specific modifications as well as to run Bayesian optimization in various applications simply. This package contains codes for several surrogate models such as Gaussian process regression and random forest regression, so that sequential model-based optimization can be implemented.
XCTimage is a Python package that provides a suite of tools and functions for the analysis of X-Ray Computed Tomography images. It contains image pre-processing and feature extraction techniques in 2 and 3 dimensional data. The package provides efficient, user-friendly feature extraction to serve as a foundation for robust, scalable machine learning pipelines. It has a broader goal of becoming the foundation for analysis of XCT imaging across domains and materials.