XML-RPC Interface to NEOS

Within this package the XML-RPC API to NEOS < https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.

Estimators and Plots for Gamma and Pareto Tail Detection

Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) and Klar (2024) . One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) , distinguishes between log-convex and log-concave tail behavior. Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) . The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values.

Calculates Robust Performance Metrics for Imbalanced Classification Problems

Calculates robust Matthews Correlation Coefficient (MCC) and robust F-Beta Scores, as introduced by Holzmann and Klar (2024) . These performance metrics are designed for imbalanced classification problems. Plots the receiver operating characteristic curve (ROC curve) together with the recall / 1-precision curve.

Functions for Clustering and Testing of Presence-Absence, Abundance and Multilocus Genetic Data

Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview.

Statistical Inference via Lancaster Correlation

Implementation of the methods described in Holzmann, Klar (2024) . Lancaster correlation is a correlation coefficient which equals the absolute value of the Pearson correlation for the bivariate normal distribution, and is equal to or slightly less than the maximum correlation coefficient for a variety of bivariate distributions. Rank and moment-based estimators and corresponding confidence intervals are implemented, as well as independence tests based on these statistics.

ACE and AVAS for Selecting Multiple Regression Transformations

Two nonparametric methods for multiple regression transform selection are provided. The first, Alternating Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. ]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. ]. A good introduction to these two methods is in chapter 16 of Frank Harrell's "Regression Modeling Strategies" in the Springer Series in Statistics. A permutation independence test is included from [Holzmann, H., Klar, B. 2025. "Lancaster correlation - a new dependence measure linked to maximum correlation". Scandinavian Journal of Statistics. 52(1):145-169 ].

Standard Conform Pure Tone Audiometry (PTA) Plots

Facilitates plotting audiometric data (mostly) by preparing the coordinate system according to standards, given e. g. in American Speech-Language-Hearing Association (2005), .

'RStudio' Addin for Building Shiny Apps per Drag & Drop

This 'RStudio' addin makes the creation of 'Shiny' and 'ShinyDashboard' apps more efficient. Besides the necessary folder structure, entire apps can be created using a drag and drop interface and customized with respect to a specific use case. The addin allows the export of the required user interface and server code at any time. By allowing the creation of modules, the addin can be used throughout the entire app development process.

Material Management

A set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.

Extraction and Analysis of Data from the Bitcoin Cash (BCH) Blockchain

Issues RPC-JSON calls to 'bitcoind', the daemon of Bitcoin Cash (BCH), to extract transaction data from the blockchain. BCH is a fork of Bitcoin that permits a greater number of transactions per second. A BCH daemon is available under an MIT license from the Bitcoin Unlimited website < https://www.bitcoinunlimited.info>.