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Extreme Value Analysis
General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016)
Classification, Regression and Feature Evaluation
A suite of machine learning algorithms written in C++ with the R interface contains several learning techniques for classification and regression. Predictive models include e.g., classification and regression trees with optional constructive induction and models in the leaves, random forests, kNN, naive Bayes, and locally weighted regression. All predictions obtained with these models can be explained and visualized with the 'ExplainPrediction' package. This package is especially strong in feature evaluation where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, and DKM. These methods can be used for feature selection or discretization of numeric attributes. The OrdEval algorithm and its visualization is used for evaluation of data sets with ordinal features and class, enabling analysis according to the Kano model of customer satisfaction. Several algorithms support parallel multithreaded execution via OpenMP. The top-level documentation is reachable through ?CORElearn.
Gaussian Mixture Models (GMM)
Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015)
Model Menu for Radiant: Business Analytics using R and Shiny
The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in 'radiant.data'.
Replicability Analysis for Multiple Studies of High Dimension
Estimation of Bayes and local Bayes false discovery rates for
replicability analysis (Heller & Yekutieli, 2014
Density Estimation via Bayesian Inference Engines
Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020)
Flexible Genotyping for Polyploids
Implements empirical Bayes approaches to genotype
polyploids from next generation sequencing data while
accounting for allele bias, overdispersion, and sequencing
error. The main functions are flexdog() and multidog(),
which allow the specification
of many different genotype distributions. Also provided are functions to
simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
functions to calculate oracle genotyping error rates, oracle_mis(), and
correlation with the true genotypes, oracle_cor(). These latter two
functions are useful for read depth calculations. Run
browseVignettes(package = "updog") in R for example usage. See
Gerard et al. (2018)
Flexible Co-Data Learning for High-Dimensional Prediction
Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties.
The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources.
Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage.
Various types of hyper shrinkage may be used for various co-data.
Co-data may be continuous or categorical.
The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types.
The model fit is used to predict for new samples.
The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection.
See Van Nee et al. (2020)
Bayesian Change-Point Detection for Process Monitoring with Fault Detection
Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023)
Bayesian reconstruction of growth velocity
A nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.