Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.

This package provides a data set package with the "Orsi" and "Park/Durand" fronts as SpatialLinesDataFrame objects. The Orsi et al. (1995) fronts are published at the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the SEANOE Altimetry-derived Antarctic Circumpolar Current fronts page, please see package CITATION for details.

Access and Analyze Official Development Assistance (ODA) data using the OECD API <https://gitlab.algobank.oecd.org/public-documentation/dotstat-migration/-/raw/main/OECD_Data_API_documentation.pdf>. ODA data includes sovereign-level aid data such as key aggregates (DAC1), geographical distributions (DAC2A), project-level data (CRS), and multilateral contributions (Multisystem).

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

Expands quoted language by recursively replacing any symbol that points to quoted language with the language it points to. The recursive process continues until only symbols that point to non-language objects remain. The resulting quoted language can then be evaluated normally. This differs from the traditional quote'/'eval pattern because it resolves intermediate language objects that would interfere with evaluation.

This package provides functions to access and download data from the Open Case Studies <https://www.opencasestudies.org/> repositories on GitHub <https://github.com/opencasestudies>. Different functions enable users to grab the data they need at different sections in the case study, as well as download the whole case study repository. All the user needs to do is input the name of the case study being worked on. The package relies on the httr::GET() function to access files through the GitHub API. The functions usethis::use_zip() and usethis::create_from_github() are used to clone and/or download the case study repositories. To cite an individual case study, please see the respective README file at <https://github.com/opencasestudies/>. <https://github.com/opencasestudies/ocs-bp-rural-and-urban-obesity> <https://github.com/opencasestudies/ocs-bp-air-pollution> <https://github.com/opencasestudies/ocs-bp-vaping-case-study> <https://github.com/opencasestudies/ocs-bp-opioid-rural-urban> <https://github.com/opencasestudies/ocs-bp-RTC-wrangling> <https://github.com/opencasestudies/ocs-bp-RTC-analysis> <https://github.com/opencasestudies/ocs-bp-youth-disconnection> <https://github.com/opencasestudies/ocs-bp-youth-mental-health> <https://github.com/opencasestudies/ocs-bp-school-shootings-dashboard> <https://github.com/opencasestudies/ocs-bp-co2-emissions> <https://github.com/opencasestudies/ocs-bp-diet>.

This package provides an interface to OpenCL, allowing R to leverage computing power of GPUs and other HPC accelerator devices.

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

This package provides a decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, expected phylogenetic diversity, the number of features that meet persistence targets, or identifying a set of projects that meet persistence targets for minimal cost. Constraints (e.g. lock in specific actions) and feature weights can also be specified to further customize prioritizations. After defining a project prioritization problem, solutions can be obtained using exact algorithms, heuristic algorithms, or random processes. In particular, it is recommended to install the Gurobi optimizer (available from <https://www.gurobi.com>) because it can identify optimal solutions very quickly. Finally, methods are provided for comparing different prioritizations and evaluating their benefits. For more information, see Hanson et al. (2019) <doi:10.1111/2041-210X.13264>.

Convert odds ratio to relative risk in cohort studies with partial data information (Wang (2013) <doi:10.18637/jss.v055.i05>).

Estimates win ratio or Mann-Whitney parameter for two group comparisons using ordered composite endpoints with right censoring as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.

Inference using a class of Hidden Markov models (HMMs) called oHMMed'(ordered HMM with emission densities <doi:10.1186/s12859-024-05751-4>): The oHMMed algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. These are modelled as discrete hidden states; the observed data points are then realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are then inferred. Relevant for application to genomic sequences, time series, or any other sequence data with serial autocorrelation.

Extract results into R from the Observational Health Data Sciences and Informatics result database (see <https://ohdsi.github.io/Strategus/results-schema/index.html>) and generate reports/presentations via quarto that summarize results in HTML format. Learn more about OhdsiReportGenerator at <https://ohdsi.github.io/OhdsiReportGenerator/>.

This package provides a database containing the names of the babies born in Ontario between 1917 and 2018. Counts of fewer than 5 names were suppressed for privacy.

Search and extract data from the Organization for Economic Cooperation and Development (OECD).

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>.

Allows users to discover and retrieve Ocean Networks Canada's oceanographic data in raw, text, image, audio, video or any other format available. Provides a class that wraps web service calls and business logic so that users can download data with a single line of code.

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>.

Combine the air quality data analysis methods of openair with the JavaScript Leaflet (<https://leafletjs.com/>) library. Functionality includes plotting site maps, "directional analysis" figures such as polar plots, and air mass trajectories.

Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.

Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) <doi:10.1093/biomet/asz065>.

This package provides a collection of aesthetically appealing color palettes for effective data visualization with ggplot2'. Palettes support both discrete and continuous data.

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.

Obtain optimum block from Non-overlapping Block Bootstrap method.