Fungible Coefficients and Monte Carlo Functions (fungible)
Functions for computing fungible coefficients and Monte Carlo data.
A Fast Implementation of Random Forests (ranger)
A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class ‘’ (R package GenABEL) can be directly analyzed.
Statistical Inference for Partially Observed Markov Processes (pomp)
Tools for working with partially observed Markov processes (POMPs, AKA stochastic dynamical systems, state-space models). ‘pomp’ provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a platform for the implementation of new inference methods.
Estimates Mixture Models for Case-Control Data (bimixt)
Estimates non-Gaussian mixture models of case-control data. The four types of models supported are binormal, two component constrained, two component unconstrained, and four component. The most general model is the four component model, under which both cases and controls are distributed according to a mixture of two unimodal distributions. In the four component model, the two component distributions of the control mixture may be distinct from the two components of the case mixture distribution. In the two component unconstrained model, the components of the control and case mixtures are the same; however the mixture probabilities may differ for cases and controls. In the two component constrained model, all controls are distributed according to one of the two components while cases follow a mixture distribution of the two components. In the binormal model, cases and controls are distributed according to distinct unimodal distributions. These models assume that Box-Cox transformed case and control data with a common lambda parameter are distributed according to Gaussian mixture distributions. Model parameters are estimated using the expectation-maximization (EM) algorithm. Likelihood ratio test comparison of nested models can be performed using the lr.test function. AUC and PAUC values can be computed for the model-based and empirical ROC curves using the auc and pauc functions, respectively. The model-based and empirical ROC curves can be graphed using the roc.plot function. Finally, the model-based density estimates can be visualized by plotting a model object created with the bimixt.model function.