Alexander C Murph

bayesWatch: Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. See: CRAN Package.
Further details: A. C. Murph, C.B. Storlie, P.M. Wilson, J.P. Williams, and J. Hannig (2024). Bayes Watch: Bayesian Change-point Detection for Process Monitoring with Fault Detection.

DeBoinR: Orders a data-set consisting of an ensemble of probability density functions on the same x-grid. Visualizes a box-plot of these functions based on the notion of distance determined by the user. Reports outliers based on the distance chosen and the scaling factor for an interquartile range rule. See: CRAN Package.
Further details: Murph, A.C., Strait, J.D., Moran, K.R., Hyman, J.D., & Stauffer, P.H. (2024) Visualisation and Outlier Detection for Probability Density Function Ensembles, Stat, 13(2), e662.

sawnuti: The SAWNUTI algorithm performs sequence comparison for finite sequences of discrete events with non-uniform time intervals. See: CRAN Package.
Further details: A. C. Murph, A. Flynt, B. R. King (2021). Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313.

autoGFD: Code to use autodifferentiation software to calculate a Generalized Fiducial Distribution from user-defined Python functions. See: Github Respository.
It is an ongoing interest of mine to debug and expand this code, and hopefully write a paper on it. If you are interested (or perhaps have an interested student), please reach out!