A Modular, Integrated Approach to Maximum Entropy Distribution
Modeling
c(
person("Julien", "Vollering", email = "julien.vollering@hvl.no", role = c("aut", "cre")),
person("Sabrina", "Mazzoni", email = "sabgis@uevora.pt", role = "aut"),
person("Rune", "Halvorsen", email = "rune.halvorsen@nhm.uio.no", role = "aut"),
person("Steven", "Phillips", role = "cph"))
Tools for training, selecting, and evaluating maximum entropy
(and standard logistic regression) distribution models. This package
provides tools for user-controlled transformation of explanatory variables,
selection of variables by nested model comparison, and flexible model
evaluation and projection. It follows principles based on the maximum-
likelihood interpretation of maximum entropy modeling, and uses infinitely-
weighted logistic regression for model fitting.
Julien Vollering [aut, cre],
Sabrina Mazzoni [aut],
Rune Halvorsen [aut],
Steven Phillips [cph]

c(
person("Julien", "Vollering", email = "julienvollering@gmail.com", role = c("aut", "cre")),
person("Sabrina", "Mazzoni", role = "aut"),
person("Rune", "Halvorsen", role = "aut"),
person("Steven", "Phillips", role = "cph"))

The Computing of Monotonic Spline Bases and Constrained
Least-Squares Estimates
c(person("Yili", "Hong", role = c("aut", "cre"),email = "yilihong@vt.edu"),person("Jie", "Min", role = c("aut", "ctb")))
Yili Hong [aut, cre],
Jie Min [aut, ctb]
Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) <doi: 10.1214/08-AOAS167>. The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative.

Microcluster-Based Detector of Anomalies in Edge Streams
This is a wrapper around the C++ implementation of 'MIDAS' (Bhatia et al., 2020) <https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf> by Siddharth Bhatia for graph like data.