A continuous version of the receiver operating characteristics (ROC) curve to visualize and assess the classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models.

A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.

In order to make it easy to use variance reduction algorithms for any simulation, this framework can help you.
    We propose user friendly and easy to extend framework. Antithetic Variates, Inner Control Variates,
    Outer Control Variates and Importance Sampling algorithms are available in the framework. User can
    write its own simulation function and use the Variance Reduction techniques in this package to
    obtain more efficient simulations. An implementation of Asian Option simulation is already 
    available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) <doi:10.1016/j.ejor.2012.03.046>.

Methods for fitting semi-parametric mean and variance models, with normal or censored data. Extended to allow a regression in the location, scale and shape parameters, and further for multiple regression in each.

Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.

Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.