impala - Bayesian Model Calibration
Package provides tools for modular Bayesian model calibration. these tools allow for posterior exploration with sampling methods including tempering and adaptive Markov Chain Monte Carlo (MCMC). Allows for pooled calibration or hierarchal calibration of parameters. For more information see Francom et al., 2025 <DOI:10.1137/24M1644092>.
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scr-3216snl-data-analysis
6.16 score 2 stars 55 scripts 285 downloads
mvBayes - Multivariate Bayesian Regression
Fit, plot, and predict a multivariate response, using an arbitrary univariate Bayesian regression model to independently fit basis components (e.g., principal components) of the response (Francom et al., 2025 <DOI:10.1137/24M1644092>).
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scr-3281snl-data-analysis
5.18 score 3 stars 10 scripts 341 downloadsveesa - Pipeline for Explainable Machine Learning with Functional Data
Implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) <doi:10.48550/arXiv.2501.07602> for technical details about the methodology.
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scr-2946snl-data-analysis
4.08 score 2 stars 249 downloadsstpphawkes - Missing Data for Marked Hawkes Process
Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).
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scr-2937snl-data-analysisgslopenblascppopenmp
3.48 score 1 stars 2 scripts 258 downloads