Previously an R wrapper of the 'ARPACK' library
    <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the
    R package 'RSpectra', an R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for solving large scale
    eigenvalue/vector problems. The current version of 'rARPACK'
    simply imports and exports the functions provided by 'RSpectra'.
    New users of 'rARPACK' are advised to switch to the 'RSpectra' package.

MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.

Inspired by Karl Broman`s reader on using 'knitr'
    with 'asciidoc'
    (<https://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is
    merely a wrapper to 'knitr' and 'asciidoc'.

Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available.

We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems.