Easy Computation of Functional Diversity Indices

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 ). Provides efficient, modular, and parallel functions to compute functional diversity indices (preprint: ).

Translate R Expressions to 'MathML' and 'LaTeX'/'MathJax'

Translate R expressions to 'MathML' or 'MathJax'/'LaTeX' so that they can be rendered in R markdown documents and shiny apps.

Accessing 'SimFin' Data

Through simfinapi, you can intuitively access the 'SimFin' Web-API (< https://www.simfin.com/>) to make 'SimFin' data easily available in R. To obtain an 'SimFin' API key (and thus to use this package), you need to register at < https://app.simfin.com/login>.

Moderation Analysis for Two-Instance Repeated Measures Designs

Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" .

Greedy Set Cover

A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.

Embed 'SWI'-'Prolog'

Interface to 'SWI'-'Prolog', < https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.

Quantile-Quantile Plot with Several Gaussian Simulations

Plots a QQ-Norm Plot with several Gaussian simulations.

Optimally Robust Estimation - Old Version

Optimally robust estimation using S4 classes and methods. Old version still needed for current versions of ROptRegTS and RobRex.

Infinitesimally Robust Estimators for Preprocessing -Omics Data

Functions for the determination of optimally robust influence curves and estimators for preprocessing omics data, in particular gene expression data (Kohl and Deigner (2010), ).

Optimally Robust Influence Curves for Regression and Scale

Functions for the determination of optimally robust influence curves in case of linear regression with unknown scale and standard normal distributed errors where the regressor is random.