When people make decisions, they may do so using a wide variety of decision rules. The package allows users to easily create obfuscation games to test the obfuscation hypothesis. It provides an easy to use interface and multiple options designed to vary the difficulty of the game and tailor it to the user's needs. For more detail: Chorus et al., 2021, Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence, Mathematical Social Sciences, 109, 28-44, <doi:10.1016/j.mathsocsci.2020.10.002>.

Reads data from Bruker OPUS binary files of Fourier-Transform infrared spectrometers of the company Bruker Optics GmbH & Co. This package is released independently from Bruker, and Bruker and OPUS are registered trademarks of Bruker Optics GmbH & Co. KG. <https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/latest-release.html>. It lets you import both measurement data and parameters from OPUS files. The main method is `read_opus()`, which reads one or multiple OPUS files into a standardized list class. Behind the scenes, the reader parses the file header for assigning spectral blocks and reading binary data from the respective byte positions, using a reverse engineering approach. Infrared spectroscopy combined with chemometrics and machine learning is an established method to scale up chemical diagnostics in various industries and scientific fields.

Various tools developed as part of the Open-CESP (Centre de recherche en Epidémiologie et Santé des Populations) initiative to generate and evaluate synthetic datasets for statistical disclosure control. This includes tools to investigate the risk-utility tradeoff achievable with given synthesis methods, as well as statistical tools to estimate (conditional) probability distributions. The main eventual aim is to help researchers and statisticians disseminate open research data.

Extends flexclust with an R implementation of order constrained solutions in k-means clustering (Steinley and Hubert, 2008, <doi:10.1007/s11336-008-9058-z>).

Ordnance Survey ('OS') is the national mapping agency for Great Britain and produces a large variety of mapping and geospatial products. Much of OS's data is available via the OS Data Hub <https://osdatahub.os.uk/>, a platform that hosts both free and premium data products. osdatahub provides a user-friendly way to access, query, and download these data.

Access data from the "City of Toronto Open Data Portal" (<https://open.toronto.ca>) directly from R.

This package provides a framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009) <doi:10.1017/S0016672309000202>.\n Wellmann, R., and Bennewitz, J. (2011) <doi:10.2527/jas.2010-3709>.\n Wellmann, R., Hartwig, S., Bennewitz, J. (2012) <doi:10.1186/1297-9686-44-34>.\n de Cara, M. A. R., Villanueva, B., Toro, M. A., Fernandez, J. (2013) <doi:10.1111/mec.12560>.\n Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) <doi:10.1017/S0016672314000196>.\n Wellmann, R. (2019) <doi:10.1186/s12859-018-2450-5>.

Predicts spatial suitability for oyster growth from environmental survey data using Analytic Hierarchy Process (AHP) weighted scoring. Users supply sensor data from Acoustic Doppler Current Profilers (ADCP), Conductivity-Temperature-Depth (CTD) sensors, bathymetric sonar, and sidescan sonar, specify a target species, and receive per-location suitability scores, a GeoTIFF heatmap for QGIS', contour lines, and a formatted PDF or HTML report. Supports fourteen species across global aquaculture regions, including Ostrea edulis, Magallana gigas, Crassostrea virginica, Crassostrea hongkongensis, and ten further species; see list_species(). Includes season-aware scoring, tidal height correction, Bayesian tolerance parameter updating from field observations, spatial block cross-validation (Roberts et al., 2017, <doi:10.1111/ecog.02881>), permutation variable importance, wave exposure and sediment stability modules, Harmful Algal Bloom (HAB) risk and anthropogenic disturbance scoring with optional live International Council for the Exploration of the Sea (ICES) data integration, hybrid larval dispersal connectivity scoring (union-find Gaussian kernel plus optional OpenDrift or Finite Volume Community Ocean Model ('FVCOM') connectivity matrix), and batch multi-species comparison.

This package provides a DBI-compatible interface to ODBC databases.

Simplifies the creation of xlsx files by providing a high level interface to writing, styling and editing worksheets.

This package provides an R interface to the OMOPHub API for accessing OHDSI ATHENA standardized medical vocabularies. Supports concept search, semantic search using neural embeddings, concept similarity, vocabulary exploration, hierarchy navigation, relationship queries, concept mappings, and FHIR-to-OMOP concept resolution with automatic pagination.

Import data from Our World in Data', an organisation which publishes research and data on global economic and social issues.

Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.

Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio.

The openMSE package is designed for building operating models, doing simulation modelling and management strategy evaluation for fisheries. openMSE is an umbrella package for the MSEtool (Management Strategy Evaluation toolkit), DLMtool (Data-Limited Methods toolkit), and SAMtool (Stock Assessment Methods toolkit) packages. By loading and installing openMSE', users have access to the full functionality contained within these packages. Learn more about openMSE at <https://openmse.com/>.

We provide an R interface to OpenML.org which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. See <https://www.openml.org/guide/api> for more information.

An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.

This package provides cohort pathway analysis for Observational Medical Outcomes Partnership (OMOP) Common Data Model databases, including both standard (post-index) and pre-index pathway analyses. The pre-index analysis identifies sequences of events occurring in a lookback window before the target cohort index date. Built on the CohortPathways analysis framework originally developed by Christopher Knoll and the Observational Health Data Sciences and Informatics community through WebAPI'. Methodological background and the originating implementation are described in <https://github.com/OHDSI/CohortPathways>.

Client for the Office of National Statistics ('ONS') API <https://api.beta.ons.gov.uk/v1>.

Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.

This package provides functions to perform subspace clustering and classification.

This package provides functions to test/check/verify/investigate the ordering of vectors. The is_[strictly_]* family of functions test vectors for sorted', monotonic', increasing', decreasing order; is_constant and is_incremental test for the degree of ordering. `ordering` provides a numeric indication of ordering -2 (strictly decreasing) to 2 (strictly increasing).

This package provides a simple wrapper for the Octopus Energy API <https://developer.octopus.energy/docs/api/>. It handles authentication, by storing a provided API key and meter details. Implemented endpoints include products for viewing tariff details and consumption for viewing meter consumption data.

It implements the online Bayesian methods for change point analysis. It can also perform missing data imputation with methods from VIM'. The reference is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>. The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.