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. This package depends on R package 'rolog', which requires an installation of the 'SWI'-'Prolog' runtime either from 'swi-prolog.org' or from R package 'rswipl'.

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'.

Explicitly Qualifying Namespaces by Automatically Adding 'pkg::' to Functions

Automatically adding 'pkg::' to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the 'RStudio' Markers pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used for checking which packages are actually used in a project.

Quantile-Quantile Plot with Several Gaussian Simulations

Plots a QQ-Norm Plot with several Gaussian simulations.

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.

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), ).

Estimating and Mapping Disaggregated Indicators

Functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise direct estimation, the model-based unit-level approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010) ), the area-level model (see "Estimates of income for small places: An application of James-Stein procedures to Census Data" by Fay and Herriot (1979) ) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel. For a detailed description of the package and the methods used see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".