Ensemble Forecast Verification for Large Datasets (easyVerification)
Set of tools to simplify application of atomic forecast verification metrics for (comparative) verification of ensemble forecasts to large datasets. The forecast metrics are imported from the ‘SpecsVerification’ package, and additional forecast metrics are provided with this package. Alternatively, new user-defined forecast scores can be implemented using the example scores provided and applied using the functionality of this package.
Minimum Hellinger Distance Test for Normality (mhde)
Implementation of a goodness-of-fit test for normality using the Minimum Hellinger Distance.
Tools for Time Series Analysis Based on Symbolic Aggregate Dicretization (jmotif)
A set of tools based on time series symbolic discretization and vector space model that aids in time series characteristic pattern discovery and facilitates interpretable time series classification.
Tools for Semantic Vector Spaces (svs)
Various tools for semantic vector spaces, such as correspondence analysis (simple, multiple and discriminant), latent semantic analysis, probabilistic latent semantic analysis, non-negative matrix factorization and EM clustering. Furthermore, there are specialized distance measures, plotting functions and some helper functions.
Learning Algorithms for Dynamic Treatment Regimes (DTRlearn)
Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by potentially time-varying patient features and intermediate outcomes observed in previous stages. There are 3 main type methods, O-learning, Q-learning and P-learning to learn the optimal Dynamic Treatment Regimes with continuous variables. This package provide these state of arts algorithms to learn DTRs.