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Optimally Robust Estimation - Old Version
Optimally robust estimation using S4 classes and methods. Old version still needed for current versions of ROptRegTS and RobRex.
Multivariate Normal Probabilities using Vecchia Approximation
Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities"
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)
Aggregate Longitudinal Survey Data
Aggregate Business Tendency Survey Data (and other qualitative surveys) to time series at various aggregation levels. Run aggregation of survey data in a speedy, re-traceable and a easily deployable way. Aggregation is substantially accelerated by use of data.table. This package intends to provide an interface that is less general and abstract than data.table but rather geared towards survey researchers.
Easy-to-Use, Dependencyless Logger
An easy-to-use 'ndjson' (newline-delimited 'JSON') logger. It provides a set of wrappers for base R's message(), warning(), and stop() functions that maintain identical functionality, but also log the handler message to an 'ndjson' log file. No change in existing code is necessary to use this package, and only a few additional adjustments are needed to fully utilize its potential.
Optimally Robust Estimation for Regression-Type Models
Optimally robust estimation for regression-type models using S4 classes and methods.
Power Analysis and Sample Size Calculation
Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005),
Miscellaneous Functions from M. Kohl
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.
Omics Data Analysis
Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015),
Statistical Classification
Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991),