Linear Predictive Models Based on the LIBLINEAR C/C++ Library

A wrapper around the LIBLINEAR C/C++ library for machine learning (available at < https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries.

Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Bootstrap Functions

Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.

Model Selection and Multimodel Inference Based on (Q)AIC(c)

Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.

Limited Memory BFGS Minimizer with Bounds on Parameters with optim() 'C' Interface

Interfacing to Nocedal et al. L-BFGS-B.3.0 (See < http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>) limited memory BFGS minimizer with bounds on parameters. This is a fork of 'lbfgsb3'. This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function (see writing 'R' extensions and source for details). This package also adds more stopping criteria as well as allowing the adjustment of more tolerances.

Data Manipulation Functions Implemented in C

Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.

'Rcpp' Integration for the 'Eigen' Templated Linear Algebra Library

R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library. Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.

'Rapidjson' C++ Header Files

Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.

General Purpose Optimization in R using C++

Perform general purpose optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying optim().

'C++' Headers for 'ggdmc' Package

A fast 'C++' implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. It enables the user to optimise the choice response time model by connecting with the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampler implemented in the 'ggdmc' package. The package fuses the hierarchical modelling, Bayesian inference, choice response time models and factorial designs, allowing users to build their own design-based models. For more information on the underlying models, see the works by Voss, Rothermund, and Voss (2004) , Ratcliff and McKoon (2008) , and Brown and Heathcote (2008) .