The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMD
Algorithm
The Tweedie lasso model implements an iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing solution paths of the (grouped) lasso and the (grouped) elastic net methods.

Health Effects of Air Pollution and Temperature (HEAT)
Timeseries analysis is conducted using Korean mortality
and environment variables

Timeseries analysis is conducted using Korean mortality and environment variables

Timeseries analysis is conducted using Korean mortality and environmental variables

c(person("Tingyu", "Li", email = "styli@ucdavis.edu", role = c("aut", "cre")), person("Xiao", "Tian", email = "xtzou@ucdavis.edu", role = "aut"), person("Gregory", "Pasternack", email = "gpast@ucdavis.edu", role = "aut"))
This tool identifies hydropeaking events from raw time-series flow record, a rapid flow variation induced by the hourly-adjusted electricity market. The novelty of 'HEDA' is to use vector angle instead of the first-order derivative to detect change points which not only largely improves the computing efficiency but also accounts for the rate of change of the flow variation. More details <doi:10.1016/j.jhydrol.2021.126392>.
Tingyu Li [aut, cre],
Xiao Tian [aut],
Gregory Pasternack [aut]

c(person("Tingyu", "Li", email = "styli@ucdavis.edu", role = c("aut", "cre")), person("Xiaotian", "Zou", email = "xtzou@ucdavis.edu", role = "aut"), person("Gregory", "Pasternack", email = "gpast@ucdavis.edu", role = "aut"))
Tingyu Li [aut, cre],
Xiaotian Zou [aut],
Gregory Pasternack [aut]

Hierarchical Ensemble Methods for Directed Acyclic Graphs
Marco Notaro [aut, cre] and Giorgio Valentini [aut]
(AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano)
An implementation of Hierarchical Ensemble Methods for DAGs: 'HTD-DAG' (Hierarchical Top Down) and 'TPR-DAG' (True Path Rule). 'HEMDAG' can be used to enhance the predictions of virtually any flat learning method, by taking into account the hierarchical nature of the classes of a bio-ontology. 'HEMDAG' is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but it can be also safely applied to tree-structured taxonomies (as FunCat), since trees are DAGs. 'HEMDAG' scale nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini, Prediction of Human Phenotype Ontology terms by means of Hierarchical Ensemble methods, BMC Bioinformatics 2017).
graph, RBGL, PerfMeas, precrec, preprocessCore, methods