**Calculations of One Discrete Model in Several Time Steps** (**MPkn**)

A matrix discrete model having the form ‘M[i+1] = (I + Q)*M[i]’. The calculation of the values of ‘M[i]’ only for pre-selected values of ‘i’. The method of calculation is presented in the vignette ‘Fundament’ (‘Base’). Maybe it`s own idea of the author of the package. A weakness is that the method gives information only in selected steps of the process. It mainly refers to cases with matrices that are not Markov chain. If ‘Q’ is Markov transition matrix, then MUPkL() may be used to calculate the steady-state distribution ‘p’ for ‘p = Q*p’. Matrix power of non integer (matrix.powerni()) gives the same results as a mpower() from package ‘matlib’. References: ‘Markov chains’, (<https://…arkov_chain#Expected_number_of_visits> ). Donald R. Burleson, Ph.D. (2005), ‘ON NON-INTEGER POWERS OF A SQUARE MATRIX’, (<http://…/Eigenvalues.htm> ).

**Bootstrap Stacking of Random Forest Models for Heterogeneous Data** (**Sstack**)

Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.

**Repertoire Dissimilarity Index** (**rdi**)

Methods for calculation and visualization of the Repertoire Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) <doi:10.1186/s12859-017-1556-5>.

**Tuned Data Mining in R** (**TDMR**)

Tuned Data Mining in R (‘TDMR’) performs the complete tuning of a data mining task (predictive analytics, that is classification and regression). Preprocessing parameters and modeling parameters can be tuned simultaneously. It incorporates a variety of tuners (among them ‘SPOT’ and ‘CMA’ with package ‘rCMA’) and allows integration of additional tuners. Noise handling in the data mining optimization process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.

**A Framework for Reproducible and Collaborative Data Science** (**workflowr**)

Combines literate programming (‘knitr’ and ‘rmarkdown’) and version control (‘Git’, via ‘git2r’) to generate a website containing time-stamped, versioned, and documented results.

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