This package provides a comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. Multidendrograms can group more than two clusters when ties in proximity data occur, and therefore they do not depend on the order of the input data. Descriptive measures to analyze the resulting dendrogram are additionally provided.

The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.

This package provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <doi:10.1177/0962280218786302> for further details.

XKCD described a supposedly "bad" colormap that it called a "Painbow" (see <https://xkcd.com/2537/>). But simple tests demonstrate that under some circumstances, the colormap can perform very well, and people can find information that is difficult to detect with the ggplot2 default and even supposedly "good" colormaps like viridis. This library let's you use the Painbow in your own ggplot graphs.

This package provides functions to simulate Pool-seq data under models of demographic formation and to import Pool-seq data from real populations. Implements two ABC algorithms for performing parameter estimation and model selection using Pool-seq data. Cross-validation can also be performed to assess the accuracy of ABC estimates and model choice. Carvalho et al., (2022) <doi:10.1111/1755-0998.13834>.

Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to Garcà a-Portugués and Meilán-Vila (2024) <doi:10.48550/arXiv.2411.04166> and Garcà a-Portugués and Meilán-Vila (2023) <doi:10.1007/978-3-031-32729-2_4>.

Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.

Estimating the Shapley values using the algorithm in the paper Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu and Wei Zheng (2024) <doi:10.1080/01621459.2023.2257364> "Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs". You provide the data and define the value function, it retures the estimated Shapley values based on sampling methods or experimental designs.

IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell.

This package provides tools for normalizing and analyzing of GeneChip Mapping 100K and 500K Set. Affymetrix GeneChip Human Mapping 100K and 500K Set allows the DNA copy number mea- surement of respectively 2× 50K and 2× 250K SNPs along the genome. Their high density allows a precise localization of genomic alterations and makes them a powerful tool for cancer and copy number polymorphism study.

The bayNorm package is used for normalizing single-cell RNA-seq data. The main function is bayNorm, which is a wrapper function for gene specific prior parameter estimation and normalization. The input is a matrix of scRNA-seq data with rows different genes and columns different cells. The output is either point estimates from posterior (2D array) or samples from posterior (3D array).

The two main functionalities of this package are creating mock objects (functions) and selectively intercepting calls to a given function that originate in some other function. It can be used with any testing framework available for R. Mock objects can be injected with either this package's own stub function or a similar with_mock facility present in the testthat package.

This package provides tools for defensive programming. It is inspired by purrr mappers and based on rlang. Attempt extends and facilitates defensive programming by providing a consistent grammar, and a set of functions for common tests and conditions. Attempt only depends on rlang, and focuses on speed, so it can be integrated with other functions and used in the data analysis.

This package provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.

This package provides a flexible interface for interacting with Large Language Model ('LLM') providers including OpenAI', Groq', Anthropic', DeepSeek', DashScope', and GitHub Models'. Supports both synchronous and asynchronous chat-completion APIs, with features such as retry logic, dynamic model selection, customizable parameters, and multi-message conversation handling. Designed to streamline integration with state-of-the-art LLM services across multiple platforms.

This package provides a tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) <doi:10.1093/gigascience/giad030>). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. eHDPrep also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies.

This repository contains software for the calculation of goodness-of-fit test statistics and their P-values. The three statistics computed are the Empirical Distribution function statistics called Cramer-von Mises, Anderson-Darling, and Watson statistics. The statistics and their P-values can be used to assess an assumed distribution.The following distributions are available: Uniform, Normal, Gamma, Logistic, Laplace, Weibull, Extreme Value, and Exponential.

Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a precondition matrix B: BAx=Bb (dim(B)=(n,m)). Implemented method is based on GMRES (Saad, Youcef; Schultz, Martin H. (1986). "GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems" <doi:10.1137/0907058>) with callback functions, i.e. no explicit A, B or b are required.

Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.

This package provides a framework for multiple hypothesis testing based on distribution of p values. It is well known that the p values come from different distribution for null and alternatives, in this package we provide functions to detect that change. We provide a method for using the change in distribution of p values as a way to detect the true signals in the data.

This package provides a collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) <doi:10.1002/gepi.22510>.

Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.

An interface to the Apache OpenNLP tools (version 1.5.3). The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text written in Java. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. See <https://opennlp.apache.org/> for more information.

Creating maps for statistical analysis such as proportional circles, choropleth, typology and flows. Some functions use shiny or leaflet technologies for dynamism and interactivity. The great features are : - Create maps in a web environment where the parameters are modifiable on the fly ('shiny and leaflet technologies). - Create interactive maps through zoom and pop-up ('leaflet technology). - Create frozen maps with the possibility to add labels.