0. Forecasting with Artificial Intelligence- The package ForAI uses machine learning methods such as artificial neural networks, support vector regression, extreme learning machines, and evolutionary algorithms for forecasting and prediction.
1. pfda: Functional Principal Components- The PFDA package for R performs functional principal component analysis with B-splines for univariate and bivariate responses. Also handled are binary responses and an additive model for extra variables.
2. Bias reduction in GLMs- brglm provides brglm.fit which is an alternative method to glm.fit that works under the usual glm interface.
Estimation is either by correcting the bias of the maximum likelihood estimates or using an adjusted-score approach to improving bias.
3. therese- The purpose of this package is to propose a method for inferring networks from expression data obtained in various experimental condition. The "cLasso" (consensus Lasso) method is implemented.
4. Diagnostic and prognostic meta-analysis- This package provides functions for diagnostic and prognostic meta-analyses. It estimates univariate, bivariate and multivariate models, and allows the aggregation of previously published prediction models with new data.
8. Gauss-Seidel Estimation of GLMM- This project implements a block-wise Gauss-Seidel (GS) algorithm to fit Generalized Linear Mixed Models (GLMMs). The GS algorithm allows for a significant performance increase and memory saving.
9. Instrument Limit of Detection Modeling- Limit of Detection (LOD) reflects an (medical diagnostic) instrument's level of sensitivity. We develop the LOD model selection and estimation tools with application to medical diagnostic instruments.
10. Bayesian spatial regression- Bayesian spatial regression (spdep2) is a package to complement existing model fitting functions in spdep, based on re-implementing MCMC techniques due to LeSage and Pace