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.

Many treatment effect estimators can be written as weighted outcomes. These weights have established use cases like checking covariate balancing via packages like cobalt'. This package takes the original estimator objects and outputs these outcome weights. It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>. This version is compatible with the grf package and provides an internal implementation of Double Machine Learning.

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>.

Supplemental functions and data for OpenIntro resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

Function library for the identification and separation of exponentially decaying signal components in continuous-wave optically stimulated luminescence measurements. A special emphasis is laid on luminescence dating with quartz, which is known for systematic errors due to signal components with unequal physical behaviour. Also, this package enables an easy to use signal decomposition of data sets imported and analysed with the R package Luminescence'. This includes the optional automatic creation of HTML reports. Further information and tutorials can be found at <https://luminescence.de>.

This package provides a method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization.

This package contains data from the May 2020 Occupational Employment and Wage Statistics data release from the U.S. Bureau of Labor Statistics. The dataset covers employment and wages across occupations, industries, states, and at the national level. Metropolitan data is not included.

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

This package provides a set of binary operators for common tasks such as regex manipulation.

After develop a ODK <https://opendatakit.org/> frame, we can link the frame to Google Sheets <https://www.google.com/sheets/about/> and collect data through Android <https://www.android.com/>. This data uploaded to a Google sheets'. odk2spss() function help to convert the odk frame into SPSS <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded Google sheets data or read data from Google sheets by using ODK frame submission_url'.

The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see <https://opendataformat.github.io/>.

Exposes some of the available OpenCV <https://opencv.org/> algorithms, such as a QR code scanner, and edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.

This package provides dates for public and school holidays for a number of countries and their subdivisions through the OpenHolidays API at <https://www.openholidaysapi.org/en/>.

An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports grading R scripts, R Markdown documents, and R Jupyter Notebooks.

This package provides a method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data.

Shiny UI to identify cliques of related constructs in repertory grid data. See Burr, King, & Heckmann (2020) <doi:10.1080/14780887.2020.1794088> for a description of the interpretive clustering (IC) method.

Non-spatial and spatial open-population capture-recapture analysis.

Wrapper functions for customizing HTML tables from the gt package to the ONSV style.

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>.

This package provides functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.

Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.

Calculating the stability of random forest with certain numbers of trees. The non-linear relationship between stability and numbers of trees is described using a logistic regression model and used to estimate the optimal number of trees.

Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (<https://platform.opentargets.org/api>).

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.