This package provides a complete pipeline for systematic bibliometric mapping of occupational health and safety (OHS) evidence. Starting from reference files exported from major bibliographic databases such as Web of Science, Scopus, PubMed, Dimensions, EBSCO, and others, orisma automates ingestion, deduplication, relevance filtering, occupational risk category extraction, bibliometric analysis, and report generation. The package is related to bibliometric science mapping and evidence synthesis workflows described by Aria and Cuccurullo (2017) <doi:10.1016/j.joi.2017.08.007>, Westgate (2019) <doi:10.1002/jrsm.1374>, and Lajeunesse (2016) <doi:10.1111/2041-210X.12472>, but adds a domain-specific occupational safety and health layer. The package implements three original bibliometric indicators: (1) the Worker-Risk Disconnection Index (WRDI), measuring the proportion of studies that characterise an occupational risk without including direct worker exposure data; (2) the Risk Category Saturation Index (RCS), measuring the relative over- or under-representation of each risk category relative to a uniform baseline; and (3) the Material-Gap Profile (MGP), measuring the ratio between a material's known hazard potential and its coverage in the occupational health literature. Two additional preventive intelligence indicators are provided: (4) the Abstract Sufficiency Score (ASS, 0-5), a cumulative hierarchical index of the preventively useful information contained in an abstract; and (5) the Bridge Article Score (0-5), identifying studies that simultaneously address technology, hazardous agent, worker population, exposure measurement, and preventive recommendations. Risk categories are extracted using a built-in occupational risk dictionary of 58 categories anchored in ISO 45001:2018, INSST, NIOSH, and EU-OSHA frameworks, organised in six blocks: Safety, Industrial Hygiene, Ergonomics, Psychosociology, Biological Hazards, and Emerging Technologies. The dictionary is user-extensible. Outputs include bilingual HTML reports, occupational risk sheets, priority reading rankings, guided extraction matrices for systematic review, and reproducibility certificates with MD5 hashes.

Allows code to be run only once on a given computer, using lockfiles. Typical use cases include startup messages shown only when a package is loaded for the very first time.

Data on the most popular baby names by sex and year, and for each state in Australia, as provided by the state and territory governments. The quality and quantity of the data varies with the state.

I tend to repeat the same code chunks over and over again. At first, this was fine for me and I paid little attention to such redundancies. A little later, when I got tired of manually replacing Linux filepaths with the referring Windows versions, and vice versa, I started to stuff some very frequently used work-steps into functions and, even later, into a proper R package. And that's what this package is - a hodgepodge of various R functions meant to simplify (my) everyday-life coding work without, at the same time, being devoted to a particular scope of application.

Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, <doi:10.1021/acs.analchem.1c00123>). With read_any(), Open Specy provides a single function for reading individual, batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction and flattening, intensity conversions, wavenumber alignment, and min-max normalization. Spectra can be identified in batch using an onboard reference library (Cowger et al. 2020, <doi:10.1177/0003702820929064>) using match_spec(). A Shiny app is available via run_app() or online at <https://www.openanalysis.org/openspecy/>.

Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations.

This package provides functions to retrieve public data from ORCID (Open Researcher and Contributor ID) records via the ORCID public API. Fetches employment history, education, works (publications, datasets, preprints), funding, peer review activities, and other public information. Returns data as structured data.table objects for easy analysis and manipulation. Replaces the discontinued rorcid package with a modern, CRAN-compliant implementation.

This package provides native access to the Open Neural Network Exchange (ONNX) Runtime <https://onnxruntime.ai/>, which is a performant engine for running machine learning models that are saved to a standardized format. Rather than interfacing with ONNX via Python', as in the official onnx package, onnxr directly interfaces with the runtime's C++ API via cpp11'. Models saved to .onnx files can be loaded and run on various backends, including CPUs and Apple's CoreML library.

Aims to support all features of the system credential store, including non-portable ones. Supports Keychain on macOS', and Credential Manager on Windows'. See the keyring package if you need a portable API'.

This package provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model.

Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle.

This package provides access to selected functions and data available through any OpenBioMaps <https://openbiomaps.org> server instance. OpenBioMaps is an open-source biodiversity data management platform designed for conservation professionals and researchers. User authentication and access control are handled through OpenBioMaps login credentials.

This package provides functionality to process text files created by Emacs Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc). Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects. Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours. Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files.

The Open University Learning Analytics Dataset (OULAD) is available from Kuzilek et al. (2017) <doi:10.1038/sdata.2017.171>. The ouladFormat package loads, cleans and formats the OULAD for data analysis (each row of the returned data set is an individual student). The packageâ s main function, combined_dataset(), allows the user to choose whether the returned data set includes assessment, demographics, virtual learning environment (VLE), or registration variables etc.

This package provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2024) <doi:10.1007/s00357-024-09473-3>.

Download and import of OpenStreetMap ('OSM') data as sf or sp objects. OSM data are extracted from the Overpass web server (<https://overpass-api.de/>) and processed with very fast C++ routines for return to R'.

Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is held-out from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (in Annals of Applied Statistics) for details of methods.

This package provides a single function options.ifunset(...) is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard options(...) function, making provision for THEIR end user to place options(...) directives within their .Rprofile file, which will not be overridden at the point when a package is loaded.

OpenTelemetry is a collection of tools, APIs, and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package contains the OpenTelemetry SDK, and exporters. Use this package to export traces, metrics, logs from instrumented R code. Use the otel package to instrument your R code for OpenTelemetry.

Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>. A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>.

Analysis of molecular marker data from model and non-model systems. For the later, it allows statistical analysis by simultaneously estimating linkage and linkage phases (genetic map construction) according to Wu and colleagues (2002) <doi:10.1006/tpbi.2002.1577>. All analysis are based on multi-point approaches using hidden Markov models.

Calculates autoecological data (optima and tolerance ranges) of a biological species given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.

Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package HDoutliers', FastPCS() from the package FastPCS', mvBACON() from robustX', adjOutlyingness() from robustbase', DectectDeviatingCells() from cellWise', covMcd() from robustbase'.

Native R tools for optimal binning workflows in predictive modeling. The package provides APIs for binary, multi-class and continuous targets, with multi-variable binning and scorecard workflows. Methods are informed by Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025> and Navas-Palencia (2021) <doi:10.48550/arXiv.2104.08619>.