Software

changepoints an R package containing several methods for change point localization.

R code for the change point procedure and the experiments described in the paper Wang, D., Yu., Y., Rinaldo, A. and Willett, R. (2019). Localizing Changes in High-Dimensional Vector Autoregressive Processes. arXiv

R code for the change point procedure and the experiments described in the paper Madrid Padilla, O.H., Yu, Y., Wang, D. And Rinaldo, A. (2019). Optimal nonparametric multivariate change point detection and localization. arXiv

R code for the change point procedure and the experiments described in the paper Madrid Padilla, O.H., Yu, Y., Wang, D. And Rinaldo, A. (2019). Optimal nonparametric change point detection and localization. arXiv

R code for the change point procedure and the experiments described in the paper Wang, D., Yu. Y. and Rinaldo, A. (2020). Optimal Change Point Detection and Localization in Sparse Dynamic Networks, to appear in the Annals of Statistics. arXiv

Topological Data Analysis (TDA), a R package for topological data analysis and density clustering. Currently maintained by Jisu Kim. For a description of the package see Fasy, B.T., Kim, J., Lecci, F. and Clement, M. (2014). Introduction to the R package TDA. arXiv

R package for conformal inference written by Ryan Tibshirani to implements the methods and re-create the experiments in Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. and Wasserman, L. (2016). Distribution-Free Predictive Inference For Regression, Journal of the American Statistical Association, 113(523), 1094-111.

DEnsity-BAsed CLustering (DeBaCl) – A Python Toolbox for Interactive Data Analysis with Level Set Trees, written by Brian P. Kent. Reference: Kent, B. P., Rinaldo, A. and Verstynen, T. (2013). DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering. arXiv

Accompanying R code for the paper Rinaldo, A., Petrovíc, S. and Fienberg, S.E. (2013). Maximum Likelihood Estimation in Network Models, Annals of Statistics, 41(3), 1085-1110.

A collection of MATLAB scripts and a GUI to re-create the plots in Rinaldo, A., Fienberg, S.E. and Zhou, Y. (2009). On the Geometry of Discrete Exponential Families with Application to Exponential Random Graph Models, Electronic Journal of Statistics, 3, 446–484. arXiv

A small MATLAB toolbox for computing the extended maximum likelihood estimator of the parameters of hierarchical log-linear models. The toolbox implements some of the routines described in the unpublished monograph Rinaldo, A. (2006). Computing Maximum Likelihood Estimates in Log-Linear Models. and was written solely for testing purposes.