Infer log-linear Poisson Graphical Model with an auxiliary data set. Hot-deck multiple imputation method is used to improve the reliability of the inference with an auxiliary dataset. Standard log-linear Poisson graphical model can also be used for the inference and the Stability Approach for Regularization Selection (StARS) is implemented to drive the selection of the regularization parameter. The method is fully described in <doi:10.1093/bioinformatics/btx819>.

Converts standardized R4SUB (R for Regulatory Submission) evidence into indicator scores, pillar scores, and a Submission Confidence Index (SCI). Provides sensitivity analysis, explainability tables, and decision band classification to answer the question: are we ready for regulatory submission.

We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.

Efficiently parses, filters, and writes binary ITCH files (Version 5.0) containing detailed financial transactions as distributed by NASDAQ to a data.table. Includes functions to interact with NASDAQ data services at <https://emi.nasdaq.com/ITCH/> and <https://emi.nasdaq.com/ITCH/Stock_Locate_Codes/>.

This package provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling.

Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.

This package provides a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.

Collection of portable choice dialog widgets.

This package provides a set of tools for working with Romanian personal numeric codes. The core is a validation function which applies several verification criteria to assess the validity of numeric codes. This is accompanied by functionality for extracting the different components of a personal numeric code. A personal numeric code is issued to all Romanian residents either at birth or when they obtain a residence permit.

An HTTP API client for Lemmy (<https://github.com/LemmyNet/lemmy>) in R. Code and documentation are generated from the official JavaScript client source (<https://github.com/LemmyNet/lemmy-js-client>).

This package provides a collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi:10.1002/sim.5902>.

Designed to create and display complex tables with R, the rtables R package allows cells in an rtables object to contain any high-dimensional data structure, which can then be displayed with cell-specific formatting instructions. Additionally, the rtables.officer package supports export formats related to the Microsoft Office software suite, including Microsoft Word ('docx') and Microsoft PowerPoint ('pptx').

An interface to iDigBio's search API that allows downloading specimen records. Searches are returned as a data.frame. Other functions such as the metadata end points return lists of information. iDigBio is a US project focused on digitizing and serving museum specimen collections on the web. See <https://www.idigbio.org> for information on iDigBio.

Predicts morphological parameters of rorquals (e.g. body mass, flipper length, maximum engulfment capacity) from body length using allometric equations from Kahane-Rapport and Goldbogen (2018) <doi:10.1002/jmor.20846>.

Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.

R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.

This package provides R-squared values and standardized regression coefficients for linear models applied to multiply imputed datasets as obtained by mice'. Confidence intervals, zero-order correlations, and alternative adjusted R-squared estimates are also available. The methods are described in Van Ginkel and Karch (2024) <doi:10.1111/bmsp.12344> and in Van Ginkel (2020) <doi:10.1007/s11336-020-09696-4>.

Reference database manager offering a set of functions to import, organize, clean, filter, audit and export reference genetic data. Provide functions to download sequence data from NCBI GenBank <https://www.ncbi.nlm.nih.gov/genbank/>. Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies.

Regularized Greedy Forest wrapper of the Regularized Greedy Forest <https://github.com/RGF-team/rgf/tree/master/python-package> python package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.

This package provides a novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) <doi:10.6339/21-JDS1014>, and regression (Ara A., et. al, 2021) <doi:10.1016/j.eswa.2022.117107> problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) <doi:10.6339/21-JDS1025>.

This package provides tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.

This package provides a native R implementation for encoding and decoding sixel graphics (<https://vt100.net/docs/vt3xx-gp/chapter14.html>), and a dedicated sixel graphics device that allows plots to be rendered directly within compatible terminal emulators.

The SaTScan'(TM) <https://www.satscan.org> software uses spatial and space-time scan statistics to detect and evaluate spatial and space-time clusters. With the rsatscan package, you can run the external SaTScan software from within R using R data formats. To successfully select appropriate parameter settings within rsatscan', you must first learn SaTScan'.

Provide seamless support for right-to-left (RTL) languages, such as Persian and Arabic, in R Markdown documents and LaTeX output. It includes functions and hooks that enable easy integration of RTL language content, allowing users to create documents that adhere to RTL writing conventions. For in-depth insights into dynamic documents and the knitr package, consider referring to Xie, Y (2014) <ISBN: 978-1-482-20353-0>.