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'.

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.

Implements sample size and power calculation methods with a focus on balance and fairness in study design, inspired by the Zoroastrian deity Rashnu, the judge who weighs truth. Supports survival analysis and various hypothesis testing frameworks.

Algorithms for the spatial stratification of landscapes, sampling and modeling of 
    spatially-varying phenomena. These algorithms offer a simple framework for the stratification 
    of geographic space based on raster layers representing landscape factors and/or factor scales. 
    The stratification process follows a hierarchical approach, which is based on first level units 
    (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric 
    techniques allow to measure the correspondence between the geographic space and the landscape 
    configuration represented by the units. These correspondence metrics are useful to define 
    sampling schemes and to model the spatial variability of environmental phenomena. The 
    theoretical background of the algorithms and code examples are presented in Fuentes et al. (2022). 
    <doi:10.32614/RJ-2022-036>.