Currently fully supports Enrichr, JASPAR, miEAA, PANTHER, Reactome, STRING, and UniProt! The goal of rbioapi is to provide a user-friendly and consistent interface to biological databases and services. In a way that insulates the user from the technicalities of using web services API and creates a unified and easy-to-use interface to biological and medical web services. This is an ongoing project; New databases and services will be added periodically. Feel free to suggest any databases or services you often use.

This package provides an interface to Blimp software for Bayesian latent variable modeling, missing data analysis, and multiple imputation. The package generates Blimp syntax, executes Blimp models, and imports results back into R as structured objects with methods for visualization and analysis. Requires Blimp software (freely available at <https://www.appliedmissingdata.com/blimp>) to be installed separately.

Download large sections of GenBank <https://www.ncbi.nlm.nih.gov/genbank/> and generate a local SQL-based database. A user can then query this database using restez functions or through rentrez <https://CRAN.R-project.org/package=rentrez> wrappers.

This header-only library provides modern, portable C++ wrappers for SIMD intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512). By placing this library in this package, we offer an efficient distribution system for Xsimd <https://github.com/xtensor-stack/xsimd> for R packages using CRAN.

Implementation of a Principal Component Analysis (PCA) in the torus via density ridge estimation. The main function, ridge_pca(), obtains the relevant density ridge for bivariate sine von Mises and bivariate wrapped Cauchy distribution models and provides the associated scores and variance decomposition. Auxiliary functions for evaluating, fitting, and sampling these models are also provided. The package provides replicability to Garcà a-Portugués and Prieto-Tirado (2023) <doi:10.1007/s11222-023-10273-9>.

Creating 3D radial visualizations of multivariate data. The package extends traditional radial coordinate visualization (RadViz) techniques to three-dimensional space, enabling enhanced exploration and analysis of high-dimensional datasets through interactive 3D plots. Zhu, Dai & Maitra (2022) <doi:10.1080/10618600.2021.2020129>.

The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.

Create and manipulate hypergraph objects. This early version of rhype allows for the output of matrices associated with the hypergraphs themselves. It also uses these matrices to calculate hypergraph spectra and perform spectral comparison. Functionality coming soon includes calculation of hyperpaths and hypergraph centrality measures.

Implementation of JQuery <https://jquery.com> and CSS styles to allow the display of fireworks on a document. Toolkit to easily incorporate celebratory splashes in Rmarkdown and shiny apps.

This package provides a RESTful API wrapper for accessing the main databases of Germany's Federal Statistical System. Supports data search functions, credential management, result caching, and handling remote background jobs for large datasets.

The APT Package Management System provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable libapt-pkg-dev installation so functionality is curtailed if such a library is not found.

This is a library to access the current API of the web speed test service GTmetrix'. It provides a convenient wrapper to start tests, get reports, and access all kinds of meta data. For more information about using the API please visit <https://gtmetrix.com/api/docs/2.0/>.

Multivariate regression methodologies including classical reduced-rank regression (RRR) studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>, reduced-rank regression via adaptive nuclear norm penalization proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>, robust reduced-rank regression (R4) proposed by She and Chen (2017) <doi:10.1093/biomet/asx032>, generalized/mixed-response reduced-rank regression (mRRR) proposed by Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>, row-sparse reduced-rank regression (SRRR) proposed by Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>, reduced-rank regression with a sparse singular value decomposition (RSSVD) proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x> and sparse and orthogonal factor regression (SOFAR) proposed by Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.

Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2023) <doi:10.1007/s11222-022-10167-2> and the nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>.

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.

Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>. NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>. Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>. Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.

Adaptation of the Matlab tsEVA toolbox developed by Lorenzo Mentaschi available here: <https://github.com/menta78/tsEva>. It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary extreme value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>. In synthesis this approach consists in: (i) transforming a non-stationary time series into a stationary one to which the stationary extreme value theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. RtsEva offers several options for trend estimation (mean, extremes, seasonal) and contains multiple plotting functions displaying different aspects of the non-stationarity of extremes.

This package provides a method for fitting the entire regularization path of the reluctant generalized additive model (RGAM) for linear regression, logistic, Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019) <arXiv:1912.01808> for details.

Easily compute an aggregate ranking (also called a median ranking or a consensus ranking) according to the axiomatic approach presented by Cook et al. (2007). This approach minimises the number of violations between all candidate consensus rankings and all input (partial) rankings, and draws on a branch and bound algorithm and a heuristic algorithm to drastically improve speed. The package also provides an option to bootstrap a consensus ranking based on resampling input rankings (with replacement). Input rankings can be either incomplete (partial) or complete. Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.

Provide function for get data from YouTube Data API <https://developers.google.com/youtube/v3/docs/>, YouTube Analytics API <https://developers.google.com/youtube/analytics/reference/> and YouTube Reporting API <https://developers.google.com/youtube/reporting/v1/reports>.

Helps users in quickly visualizing risk-of-bias assessments performed as part of a systematic review. It allows users to create weighted bar-plots of the distribution of risk-of-bias judgments within each bias domain, in addition to traffic-light plots of the specific domain-level judgments for each study. The resulting figures are of publication quality and are formatted according the risk-of-bias assessment tool use to perform the assessments. Currently, the supported tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019) <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 (for diagnostic accuracy studies; Whiting et al (2011) <doi:10.7326/0003-4819-155-8-201110180-00009>).

Set of functions that enable you to use the FUSION commands (Program available in: <http://forsys.sefs.uw.edu/fusion/fusionlatest.html>).

Implementation of an algorithm in two steps to estimate parameters of a model whose latent dynamics are inferred through latent processes, jointly regularized. This package uses Monolix software (<https://monolixsuite.slp-software.com/>), which provide robust statistical method for non-linear mixed effects modeling. Monolix must have been installed prior to use.

This package provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive shiny application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731).