Create compressed, interactive HTML (Hypertext Markup Language) reports with embedded Python code, custom JS ('JavaScript') and CSS (Cascading Style Sheets), and wrappers for CanvasXpress plots, networks and more. Based on <https://pypi.org/project/py-report-html/>, its sister project.

Testing homogeneity of k multivariate distributions is a classical and challenging problem in statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size. We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. These tests are easy to implement and distribution-free in finite sample situations. Under appropriate regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2021) <doi:10.1016/j.jmva.2021.104897>; Soham Sarkar and Anil K Ghosh (2019) <doi:10.1109/TPAMI.2019.2912599>; William M Rand (1971) <doi:10.1080/01621459.1971.10482356>; Cyrus R Mehta and Nitin R Patel (1983) <doi:10.2307/2288652>; Joseph C Dunn (1973) <doi:10.1080/01969727308546046>; Sture Holm (1979) <doi:10.2307/4615733>; Yoav Benjamini and Yosef Hochberg (1995) <doi: 10.2307/2346101>.

Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. For more information, see Brand, Xu, Koch, and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package first started as a fork of the causalTree package on GitHub and we greatly appreciate the authors for their extremely useful and free package.

Fits area-level Hierarchical Bayesian Small Area Estimation models. The methodological foundation follows the standard area-level Small Area Estimation literature, primarily Rao and Molina (2015, ISBN: 9781118735787) <doi:10.1002/9781118735855>, while computational implementation is adapted to the parameterisation and prior-specification conventions of the brms package <doi:10.18637/jss.v080.i01>, which targets the Stan back-end. Supports a principled Bayesian workflow <doi:10.48550/arXiv.2011.01808>, with prior predictive checks, convergence diagnostics, model comparison, spatial random effects, custom distributions, missing-data handling, and a bilingual shiny application for non-programmer analysts.

This package provides a Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.

This package provides a streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database <https://haplostats.org/haplostats>. High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting HLAMatchMaker <http://www.epitopes.net> versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality.

Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, hdMTD includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>.

LecÈ iile prof/cls trebuie completate cu un câmp "ora", astfel ca oricare douÄ lecÈ ii prof/cls/ora sÄ nu se suprapunÄ Ã®ntr-o aceeaÈ i orÄ . The prof/cls lessons must be completed with a "hour" field ('ora), so that any two prof/cls/ora lessons do not overlap in the same hour. <https://vlad.bazon.net/>.

Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.

This package implements hierarchical Bayesian inference, robust frequentist inference, and distribution-free inference for dependent and unbalanced data under strong-mixing conditions. Supports triangular-array, weighted-sum and mixingale convergence regimes with Whittle and composite likelihoods, heteroskedasticity-and-autocorrelation-consistent variance estimation, block bootstrap with automatic block length, fixed-bandwidth HAR inference, adaptive conformal prediction, Bayesian decision under Region of Practical Equivalence, bridge-sampling Bayes factors, and predictive comparison via the Widely Applicable Information Criterion and leave-future-out cross-validation. Methods follow Andrews (1991) <doi:10.2307/2938229>, Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, Patton, Politis and White (2009) <doi:10.1080/07474930802459016>, Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>, Kruschke (2018) <doi:10.1177/2515245918771304>, and Gibbs and Candes (2021) <doi:10.48550/arXiv.2106.00170>.

This package provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. The package implements classical normal-approximation procedures, scale-invariant procedures, normal-reference procedures based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens-Fisher and GLHT settings. Implemented two-sample normal-approximation and scale-invariant procedures include Bai and Saranadasa (1996) <https://www.jstor.org/stable/24306018>, Chen and Qin (2010) <doi:10.1214/09-aos716>, Srivastava and Du (2008) <doi:10.1016/j.jmva.2006.11.002>, and Srivastava et al. (2013) <doi:10.1016/j.jmva.2012.08.014>. Implemented two-sample normal-reference procedures include Zhang, Guo, Zhou and Cheng (2020) <doi:10.1080/01621459.2019.1604366>, Zhang, Zhou, Guo and Zhu (2021) <doi:10.1016/j.jspi.2020.11.008>, Zhang, Zhu and Zhang (2020) <doi:10.1016/j.ecosta.2019.12.002>, Zhang, Zhu and Zhang (2023) <doi:10.1080/02664763.2020.1834516>, Zhang and Zhu (2022) <doi:10.1080/10485252.2021.2015768>, Zhang and Zhu (2022) <doi:10.1007/s42519-021-00232-w>, and Zhu, Wang and Zhang (2023) <doi:10.1007/s00180-023-01433-6>. Implemented GLHT normal-approximation procedures include Fujikoshi et al. (2004) <doi:10.14490/jjss.34.19>, Srivastava and Fujikoshi (2006) <doi:10.1016/j.jmva.2005.08.010>, Yamada and Srivastava (2012) <doi:10.1080/03610926.2011.581786>, Schott (2007) <doi:10.1016/j.jmva.2006.11.007>, and Zhou, Guo and Zhang (2017) <doi:10.1016/j.jspi.2017.03.005>. Implemented GLHT normal-reference procedures include Zhang, Guo and Zhou (2017) <doi:10.1016/j.jmva.2017.01.002>, Zhang, Zhou and Guo (2022) <doi:10.1016/j.jmva.2021.104816>, Zhu, Zhang and Zhang (2022) <doi:10.5705/ss.202020.0362>, Zhu and Zhang (2022) <doi:10.1007/s00180-021-01110-6>, Zhang and Zhu (2022) <doi:10.1016/j.csda.2021.107385>, and Cao et al. (2024) <doi:10.1007/s00362-024-01530-8>. The package also includes the random-integration normal-approximation GLHT procedure of Li et al. (2025) <doi:10.1007/s00362-024-01624-3>. A package-level overview is given in Wang, Zhu and Zhang (2026) <doi:10.1016/j.csda.2025.108269>.

This package implements various heuristics like Take The Best and unit-weight linear, which do two-alternative choice: which of two objects will have a higher criterion? Also offers functions to assess performance, e.g. percent correct across all row pairs in a data set and finding row pairs where models disagree. New models can be added by implementing a fit and predict function-- see vignette. Take The Best was first described in: Gigerenzer, G. & Goldstein, D. G. (1996) <doi:10.1037/0033-295X.103.4.650>. All of these heuristics were run on many data sets and analyzed in: Gigerenzer, G., Todd, P. M., & the ABC Group (1999). <ISBN:978-0195143812>.

Calculates a sizing function based on the number of independent sets in the rejected hypergraph (Organ, Kenney & Gu, 2026, <doi:10.48550/arXiv.2606.20514>). The sizing function is designed to be used with the GLSUP package.

This package provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related candisc package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.

This package provides a bootstrapper that launches a command-line coding agent of the user's choice in a terminal tab pre-configured for a professional R role. Each role is described by a curated harness: a subset of community skills, a system prompt, a folder layout, and quality gates. The package does not run an agent loop and does not call a language model; it discovers the chosen coder binary, generates its configuration, links the curated skills, and opens the terminal. Code written by the agent is run manually by the user, by design, so that every generated script passes through a human audit gate before execution.

This package provides a shiny application, which allows you to perform single- and multi-omics analyses using your own omics datasets. After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended Holomics workflow. Methods used in the package were implemented in the package mixomics by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752> and are described there in further detail.

Computes the test statistic and p-values of the one-sample and two-sample Hausdorff (H) goodness-of-fit tests. The H statistic measures the Hausdorff distance under the Chebyshev (l-infinity) metric, between the two cumulative distribution functions (cdfs) underlying the corresponding one-sample and two-sample null hypothesis. It coincides to the side length of the largest axis-aligned square (hypercube) that can be inscribed between the two cdfs. The following cases are covered: (i) one-sample, univariate; (ii) two-sample univariate; and (iii) two-sample bivariate. Exact one-sample p-values are computed in O(n^2 log n) time via the Exact-KS-FFT method of Dimitrova, Kaishev, and Tan (2020) <doi:10.18637/jss.v095.i10>; two-sample p-values are obtained by permutation. A key advantage of the H test is that its sensitivity can be directed towards the left tail, body, or right tail of the distribution by tuning a scale parameter sigma, and therefore maximizing its power which as shown numerically is significantly higher than the power of the classical tests such as the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling test, especially when the right tail of the distribution is targeted. The sensitivity of the test (left tail, body, or right tail) is governed by two parameters psi1 and psi2, whose values needs to be input. Then the optimal value of the scale parameter sigma is automatically computed.

This package provides a user-friendly interface for the Hierarchical Data Format 5 ('HDF5') library designed to "just work." It bundles the necessary system libraries to ensure easy installation on all platforms. Features smart defaults that automatically map R objects (vectors, matrices, data frames) to efficient HDF5 types, removing the need to manage low-level details like dataspaces or property lists. Uses the HDF5 library developed by The HDF Group <https://www.hdfgroup.org/>.

The Hierarchical Neyman-Pearson (H-NP) classification framework extends the Neyman-Pearson classification paradigm to multi-class settings where classes have a natural priority ordering. This is particularly useful for classification in unbalanced dataset, for example, disease severity classification, where under-classification errors (misclassifying patients into less severe categories) are more consequential than other misclassifications. The package implements H-NP umbrella algorithms that controls under-classification errors under user specified control levels with high probability. It supports the creation of H-NP classifiers using scoring functions based on built-in classification methods (including logistic regression, support vector machines, and random forests), as well as user-trained scoring functions.

An R port of the hashids library. hashids generates YouTube-like hashes from integers or vector of integers. Hashes generated from integers are relatively short, unique and non-seqential. hashids can be used to generate unique ids for URLs and hide database row numbers from the user. By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other. hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.

We provide extensions to the classical dataset "Example 4: Death by the kick of a horse in the Prussian Army" first used by Ladislaus von Bortkeiwicz in his treatise on the Poisson distribution "Das Gesetz der kleinen Zahlen", <DOI:10.1017/S0370164600019453>. As well as an extended time series for the horse-kick death data, we also provide, in parallel, deaths by falling from a horse and by drowning.

This package provides a self-contained, static build of the HDF5 (Hierarchical Data Format 5) C library (release 2.1.1) for R package developers. Designed for use in the LinkingTo field, it enables zero-dependency integration by building the library entirely from source during installation. Additionally, it compiles and internally links a comprehensive suite of advanced compression filters and their HDF5 plugins (Zstd, LZ4, Blosc/Blosc2, Snappy, ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are integrated out-of-the-box, allowing downstream packages to utilize high-performance compression directly through the standard HDF5 API while keeping the underlying third-party headers fully encapsulated. HDF5 is developed by The HDF Group <https://www.hdfgroup.org/>.

Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion. The main idea is to use changing intensity ratios of ion pairs from peak list generated with xcms as candidates and evaluate those against base peak chromatograms and spectra information within the raw measurement data automatically. The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.

For supersonic aircraft, flying subsonic over land, find the best route between airports. Allow for coastal buffer and potentially closed regions. Use a minimal model of aircraft performance: the focus is on time saved versus subsonic flight, rather than on vertical flight profile. For modelling and forecasting, not for planning your flight!