RDKit is a C++ and Python library for cheminformatics, which includes (among other things) the analysis and modification of molecules in 2D and 3D and descriptor generation for machine learning.

This package provides a modern module system for R. Organize code into hierarchical, composable, reusable modules, and use it effortlessly across projects via a flexible, declarative dependency loading syntax.

This is a package containing Public Key Infrastructure functions such as verifying certificates, RSA encryption and signing, which can be used to build PKI infrastructure and perform cryptographic tasks.

This package provides a suite of tools designed to build attractive command line interfaces (CLIs). It includes tools for drawing rules, boxes, trees, and Unicode symbols with ASCII alternatives.

Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, <doi:10.1093/bioinformatics/btw429>.

This package provides an interface to data provided by the Bank for International Settlements <https://www.bis.org>, allowing for programmatic retrieval of a large quantity of (central) banking data.

Run basic pattern analyses on character sets, digits, or combined input containing both characters and numeric digits. Useful for data cleaning and for identifying columns containing multiple or nonstandard formats.

Seasonal- and calendar adjustment of time series with daily frequency using the DSA approach developed by Ollech, Daniel (2018): Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.

Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.

Latent budget analysis is a method for the analysis of a two-way contingency table with an exploratory variable and a response variable. It is specially designed for compositional data.

Simple helpers for matrix multiplication on data.frames. These allow for more concise code during low level mathematical operations, and help ensure code is more easily read, understood, and serviced.

This package provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

This package implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.

Allows the comparison of data cohorts (DC) against a Counter Factual Model (CFM) and measures the difference in terms of an efficacy parameter. Allows the application of Personalised Synthetic Controls.

Following the method of Bailey et al., computes for a collection of candidate models the probability of backtest overfitting, the performance degradation and probability of loss, and the stochastic dominance.

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.

This package provides a menu-driven program and library of functions for carrying out convergence diagnostics and statistical and graphical analysis of Markov chain Monte Carlo (MCMC) sampling output.

This package provides alternative implementations of some base R functions, including sort, order, and match. The functions are simplified but can be faster or have other advantages.

Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test).

This package provides a transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.

Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve. Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" <doi:10.1016/S0146-664X(72)80017-0>. David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" <doi:10.3138/FM57-6770-U75U-7727>.

This package performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.

Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A recent description of CIU can be found in e.g. Främling (2020) <arXiv:2009.13996>.