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.

Two-part system for first collecting then managing direct observation data, as described by Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

Interface with the One Health VBD (vector-borne disease) Hub <https://vbdhub.org/> and related repositories (VectorByte <https://www.vectorbyte.org>, GBIF <https://www.gbif.org> and AREAdata <https://pearselab.github.io/areadata/>) directly to find, download, and subset vector-borne disease data.

This package provides a utility to quickly obtain clean and tidy sports odds from The Odds API <https://the-odds-api.com>.

Supports the analysis of Oceanographic data, including ADCP measurements, measurements made with argo floats, CTD measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the UNESCO or TEOS-10 equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.

The olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictorsâ regardless of relevanceâ potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective modelâ based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome. A Python version is also available at <https://pypi.org/project/olr>.

The openFDA API facilitates access to Federal Drug Agency (FDA) data on drugs, devices, foodstuffs, tobacco, and more with httr2'. This package makes the API easily accessible, returning objects which the user can convert to JSON data and parse. Kass-Hout TA, Xu Z, Mohebbi M et al. (2016) <doi:10.1093/jamia/ocv153>.

This package provides a set of tools to extract bibliographic content from OpenAlex database using API <https://docs.openalex.org>.

Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. OptGS provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) <doi:10.18637/jss.v066.i02>.

In bulk epigenome/transcriptome experiments, molecular expression is measured in a tissue, which is a mixture of multiple types of cells. This package tests association of a disease/phenotype with a molecular marker for each cell type. The proportion of cell types in each sample needs to be given as input. The package is applicable to epigenome-wide association study (EWAS) and differential gene expression analysis. Takeuchi and Kato (submitted) "omicwas: cell-type-specific epigenome-wide and transcriptome association study".

It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y.

This package implements orbit counting using a fast combinatorial approach. Counts orbits of nodes and edges from edge matrix or data frame, or a graph object from the graph package.

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

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

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

Computes A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The algorithms used in this package are based on the treatment exchange and array exchange algorithms of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617>. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.

Useful functions for one-sample (individual level data) Mendelian randomization and instrumental variable analyses. The package includes implementations of; the Sanderson and Windmeijer (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic, the multiplicative structural mean model Hernán and Robins (2006) <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor substitution and two-stage residual inclusion estimators explained by Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.

Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported. Additionally raster maps may be constructed using custom tile servers. Maps can be plotted using either base graphics, or ggplot2. This package is not affiliated with the OpenStreetMap.org mapping project.

This package implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) <doi:10.1007/s10742-022-00284-w> and Griffin et al. (2022) <doi:10.1186/s12874-021-01471-y> for a description of our methods.

This package provides functions to download and tidy statistical data published by the Office for National Statistics <https://www.ons.gov.uk>. Covers GDP, inflation (CPI, CPIH, RPI), unemployment, employment, wages, trade, retail sales, house prices, productivity, population, and public sector finances. Most series are fetched from the ONS website using its CSV time series endpoint. House price data is sourced from HM Land Registry <https://www.gov.uk/government/organisations/land-registry>. Data is cached locally between sessions.

Distributed reproducible computing framework, adopting ideas from git, docker and other software. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans.

Introduces optional types with some() and none, as well as match_with() from functional languages.

This package performs outrigger local polynomial regression/ distributional adaptation, using a score-matching spline estimator of the conditional score function. Details of the method can be found in Young, Shah and Samworth (2026) <doi:10.48550/arXiv.2603.11282>.

Microarray probe ID is not convenient for further enrichment analysis and target gene selection. The package is created for the rice microarray probe ID conversion. This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research. For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes. Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.