Computes the Conover-Iman test (1979) for 0th-order stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis omnibus test for i0th-order stochastic dominance among k groups (Kruskal and Wallis, 1952). conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference and for mean difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.

Iterate and repel visually similar colors away in various ggplot2 plots. When many groups are plotted at the same time on multiple axes, for instance stacked bars or scatter plots, effectively ordering colors becomes difficult. This tool iterates through color combinations to find the best solution to maximize visual distinctness of nearby groups, so plots are more friendly toward colorblind users. This is achieved by two distance measurements, distance between groups within the plot, and CIELAB color space distances between colors as described in Carter et al., (2018) <doi:10.25039/TR.015.2018>.

Responsive and modern HTML card essentials for shiny applications and dashboards. This novel card component in Bootstrap provides a flexible and extensible content container with multiple variants and options for building robust R based apps e.g for graph build or machine learning projects. The features rely on a combination of JQuery <https://jquery.com> and CSS styles to improve the card functionality.

An interface for creating, registering, and resolving content-based identifiers for data management. Content-based identifiers rely on the cryptographic hashes to refer to the files they identify, thus, anyone possessing the file can compute the identifier using a well-known standard algorithm, such as SHA256'. By registering a URL at which the content is accessible to a public archive (such as Hash Archive) or depositing data in a scientific repository such Zenodo', DataONE or SoftwareHeritage', the content identifier can serve many functions typically associated with A Digital Object Identifier ('DOI'). Unlike location-based identifiers like DOIs', content-based identifiers permit the same content to be registered in many locations.

This high-level API client provides open access to cryptocurrency market data, sentiment indicators, and interactive charting tools. The data is sourced from major cryptocurrency exchanges via curl and returned in xts'-format. The data comes in open, high, low, and close (OHLC) format with flexible granularity, ranging from seconds to months. This flexibility makes it ideal for developing and backtesting trading strategies or conducting detailed market analysis.

Chromosome files in the Fasta format usually contain large sequences like human genome. Sometimes users have to split these chromosomes into different files according to their chromosome number. The chromseq can help to handle this. So the selected chromosome sequence can be used for downstream analysis like motif finding. Howard Y. Chang(2019) <doi:10.1038/s41587-019-0206-z>.

CemCO algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).

Several functions are available for calculating the most widely used effect sizes (ES), along with their variances, confidence intervals and p-values. The output includes ES's of d (mean difference), g (unbiased estimate of d), r (correlation coefficient), z (Fisher's z), and OR (odds ratio and log odds ratio). In addition, NNT (number needed to treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are computed. This package uses recommended formulas as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). A free web application is available at <https://acdelre.github.io/apps/compute_es/>.

An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.

Non-parametric test for equality of multivariate distributions. Trains a classifier to classify (multivariate) observations as coming from one of several distributions. If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.

This package implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs.

This package provides a comprehensive toolkit for political linguistics featuring a museum of famous digital gaffes, phonetic transformation algorithms (Soundex, consonant shifts), QWERTY keyboard geometry for typo simulation, syllable parsing, word blending (portmanteau creation), and text corruption analysis. Originally inspired by the infamous "covfefe" tweet of 2017.

This package implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.

C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).

This package provides tools to simulate child-penalty data and estimate DID, TD, and NTD identification frameworks from Leventer (2025), "Identification of Child Penalties" <doi:10.48550/arXiv.2602.07486>.

Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026> and Laborde et al. (2023) <doi: 10.1186/s12859-023-05210-6>. Such methods can inform whether clustering algorithms are appropriate for a data set.

This package provides an interface to the ClinicalOmicsDB API, allowing for easy data downloading and importing. ClinicalOmicsDB is a database of clinical and omics data from cancer patients. The database is accessible at <http://trials.linkedomics.org>.

Decorate functions to make them return enhanced output. The enhanced output consists in an object of type chronicle containing the result of the function applied to its arguments, as well as a log detailing when the function was run, what were its inputs, what were the errors (if the function failed to run) and other useful information. Tools to handle decorated functions are included, such as a forward pipe operator that makes chaining decorated functions possible.

Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC.

This package implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) <doi:10.1109/TNB.2018.2845126>, and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) <doi:10.1109/TNB.2020.3031202>. Further details on how to use them are given in the accompanying vignette.

This package provides tools for working with observational health data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax. Common data model database table references are stored in a single compound object along with metadata.

Price credit default swaps using C code from the International Swaps and Derivatives Association CDS Standard Model. See <https://www.cdsmodel.com/cdsmodel/documentation.html> for more information about the model and <https://www.cdsmodel.com/cdsmodel/cds-disclaimer.html> for license details for the C code.

In computer experiments space-filling designs are having great impact. Most popularly used space-filling designs are Uniform designs (UDs), Latin hypercube designs (LHDs) etc. For further references one can see Mckay (1979) <DOI:10.1080/00401706.1979.10489755> and Fang (1980) <https://cir.nii.ac.jp/crid/1570291225616774784>. In this package, we have provided algorithms for generate efficient LHDs and UDs. Here, generated LHDs are efficient as they possess lower value of Maxpro measure, Phi_p value and Maximum Absolute Correlation (MAC) value based on the weightage given to each criterion. On the other hand, the produced UDs are having good space-filling property as they always attain the lower bound of Discrete Discrepancy measure. Further, some useful functions added in this package for adding more value to this package.

Solves optimal pairing and matching problems using linear assignment algorithms. Provides implementations of the Hungarian method (Kuhn 1955) <doi:10.1002/nav.3800020109>, Jonker-Volgenant shortest path algorithm (Jonker and Volgenant 1987) <doi:10.1007/BF02278710>, Auction algorithm (Bertsekas 1988) <doi:10.1007/BF02186476>, cost-scaling (Goldberg and Kennedy 1995) <doi:10.1007/BF01585996>, scaling algorithms (Gabow and Tarjan 1989) <doi:10.1137/0218069>, push-relabel (Goldberg and Tarjan 1988) <doi:10.1145/48014.61051>, and Sinkhorn entropy-regularized transport (Cuturi 2013) <doi:10.48550/arxiv.1306.0895>. Designed for matching plots, sites, samples, or any pairwise optimization problem. Supports rectangular matrices, forbidden assignments, data frame inputs, batch solving, k-best solutions, and pixel-level image morphing for visualization. Includes automatic preprocessing with variable health checks, multiple scaling methods (standardized, range, robust), greedy matching algorithms, and comprehensive balance diagnostics for assessing match quality using standardized differences and distribution comparisons.