Wrapper around the Open Source Routing Machine (OSRM) API <http://project-osrm.org/>. osrmr works with API versions 4 and 5 and can handle servers that run locally as well as the OSRM webserver.

Maps of Australian coastline and administrative regions. Data can be drawn or accessed directly as simple features objects. Includes simple functions for country or state maps of Australia and in-built data sets of administrative regions from the Australian Bureau of Statistics <https://www.abs.gov.au/>. Layers include electoral divisions and local government areas, simplified from the original sources but with sufficient detail to allow mapping of a local municipality.

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>.

Two-stage design for single-arm phase II trials with time-to-event endpoints (e.g., clinical trials on immunotherapies among cancer patients) can be calculated using this package. Two notable advantages of the package: 1) It provides flexible choices from three design methods (optimal, minmax, and admissible), and 2) the power of the design is more accurately calculated using the exact variance in the one-sample log-rank test. The package can be used for 1) planning the sample sizes and other design parameters, and 2) conducting the interim and final analyses for the Go/No-go decisions. More details about the design method can be found in: Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020). <doi:10.1002/pst.1983>.

This package provides functions to design and simulate optimal two-stage randomized controlled trials (RCTs) with ordered categorical outcomes, using rank-based tests and interim decision rules for futility and superiority, following Park (2025) <doi:10.1371/journal.pone.0318211>. The functions rule()', op() and design_table() provide a single entry point for constructing designs, evaluating their operating characteristics, and tabulating several designs at once. The separate functions provided for each combination of test statistic and stopping rule up to version 1.0.2 were deprecated in 1.0.3 and are removed here. Please see the package reference manual and the vignette for details.

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>).

Estimates optimal classification (Poole 2000) <doi:10.1093/oxfordjournals.pan.a029814> scores from roll call votes supplied though a rollcall object from package pscl'.

This package provides a visualization tool for multivariate data. This package maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multi-criteria decision-making.

Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2025, <doi:10.1021/acs.analchem.5c00962>). 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 using match_spec(). A bundled Shiny app is available via run_app() or online at <https://www.openanalysis.org/OpenSpecyV2/>.

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.

Calculate the ratio of iron oxides, hematite and goethite, in soil using the diffuse reflectance technique. The Kubelka-Munk theory, second derivative analysis, and spectral region amplitudes related to hematite and goethite content are used for quantification (Torrent, J., & Barron, V. (2008) <doi:10.2136/sssabookser5.5.c13>). Additionally, the package calculates soil color in the visible spectrum using Munsell and RGB color spaces, based on color theory (Viscarra et al. (2006) <doi:10.1016/j.geoderma.2005.07.017>).

We proposes a framework that provides real time support for early detection of anomalous series within a large collection of streaming time series data. By definition, anomalies are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the system's typical behaviour using a representative sample of the typical behaviour of the system. An approach based on extreme value theory is used for this boundary prediction process. Then a sliding window is used to test for anomalous series within the newly arrived collection of series. Feature based representation of time series is used as the input to the model. To cope with concept drift, the forecast boundary for the system's typical behaviour is updated periodically. More details regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) <doi:10.1080/10618600.2019.1617160>.

Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype() is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS() is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS() can take the results from calcPhenotype() and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. The package is described in Maeser et al. (2021) "oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data" <doi:10.1093/bib/bbab260>. calcPhenotype() - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS() - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS() - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Pure-R implementation of univariate binary-class ODA (UniODA), univariate multiclass ODA (MultiODA), and binary Classification Tree Analysis (CTA). Supports ordered and categorical attributes, priors-on inverse-frequency weighting, MAXSENS / SAMPLEREP / first-identified tie-breaking, true leave-one-out cross-validation, and Monte Carlo Fisher-randomization p-values. Covered UniODA, MultiODA, and binary CTA fixtures are tested for parity against MegaODA.exe and CTA.exe outputs.

Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included shiny application allows users to simulate outcomes given different ordinal data distributions.

Estimates rates for continuous character evolution under Brownian motion and Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al. (2012).

Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. (2020) <doi:10.1098/rsif.2020.0084>).

Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.

Evaluates the Owen Q-function for an integer value of the degrees of freedom, by applying Owen's algorithm (1965) <doi:10.1093/biomet/52.3-4.437>. It is useful for the calculation of the power of equivalence tests.

This package provides a tidy, long-format dataset of every athlete-event participation in the modern Olympic Games, spanning Athens 1896 through Milano-Cortina 2026 (about 315,000 rows). Extends the rgriff23 Olympic_history dataset (1896-2016) with five additional editions scraped from Olympedia (<https://www.olympedia.org>): PyeongChang 2018, Tokyo 2020, Beijing 2022, Paris 2024, and Milano-Cortina 2026. Companion datasets cover edition-level metadata and verified medal tables for every edition from 1896 to 2026.

Optimal Subset Cardinality Regression (OSCAR) models offer regularized linear regression using the L0-pseudonorm, conventionally known as the number of non-zero coefficients. The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization as described in Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization as in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families are supported: Cox, logistic, and Gaussian.

Open Location Codes <http://openlocationcode.com/> are a Google-created standard for identifying geographic locations. olctools provides utilities for validating, encoding and decoding entries that follow this standard.

Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) <doi:10.1101/2022.12.13.520253>.

Data used in compiling the Handbook of UK Urban Tree Allometric Equations and Size Characteristics (Fennel 2024). The data include measurements of height, crown radius and diameter at breast height (DBH) for UK urban trees. For more details see Fennell (2024) Handbook of UK Urban Tree Allometric Equations and Size Characteristics (Version 1.4). <doi:10.13140/RG.2.2.28745.04961>.