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Groovy UpCloud core provides a way to talk to the UpCloud API and a representation of the Resources
Last Release on Jul 6, 2018
Use Newton's method and coordinate descent to solve the regularized inverse correlation matrix estimation problem. Please refer to: Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural Information Processing Systems 24, 2011, p. 2330--2338.
Last Release on Feb 14, 2021
Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating 24 measures describing the features of the trajectories; (2) using factor analysis to select a subset of the 24 measures and (3) using cluster analysis to identify clusters of trajectories, and classify each individual trajectory in one of the clusters.
Last Release on Feb 15, 2021
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). ...
Last Release on Feb 15, 2021
Datasets designed to be used in conjunction with Mango Solutions training materials and the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).
Last Release on Feb 14, 2021
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
Last Release on Jul 20, 2012
WebTest Fixtures are an extension to FIT/FitNesse that implement a customer-friendly language for web testing, utilising Selenium Remote Control. WebTest runs inside FitNesse, allowing you to integrate web UI tests into an automated build system, and use other FitNesse fixtures to talk to your domain model, prepare and verify database changes during Web UI tests. Both .NET and Java FitNesse runners are supported, and the library is released under GPL.
Last Release on Jan 1, 2009
ding talk client
Last Release on Nov 17, 2021