Small/Large Sample Portfolio Optimization (PortfolioOptim)
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.

Text Corpus Analysis (corpus)
Text corpus data analysis, with full support for UTF8-encoded Unicode text. The package provides the ability to seamlessly read and process text from large JSON files without holding all of the data in memory simultaneously.

Model-Free Reinforcement Learning (ReinforcementLearning)
Performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay.

Create Pivot Tables in R (pivottabler)
Create regular pivot tables with just a few lines of R. More complex pivot tables can also be created, e.g. pivot tables with irregular layouts, multiple calculations and/or derived calculations based on multiple data frames.

Welch-James Statistic for Robust Hypothesis Testing under Heterocedasticity and Non-Normality (<a href="” target=”top”>welchADF)
Implementation of Johansen’s general formulation of Welch-James’s statistic with Approximate Degrees of Freedom, which makes it suitable for testing any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://…/Program.pdf> and published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.

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