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 general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications. This implementation was first described by Coyle and Hejazi (2018) <doi:10.21105/joss.00512>.

Convenient download functions enabling access Open Source Asset Pricing (OpenAP) data. This package enables users to download predictor portfolio returns (over 200 cross-sectional predictors with multiple portfolio construction methods) and firm characteristics (over 200 characteristics replicated from the academic asset pricing literature). Center for Research in Security Prices (CRSP)-based variables such as Price, Size, and Short-term Reversal can be downloaded with a Wharton Research Data Services (WRDS, <https://wrds-www.wharton.upenn.edu/>) subscription. For a full list of what is available, see <https://www.openassetpricing.com/>.

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

The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.

Seamlessly build and manipulate graph structures, leveraging its high-performance methods for filtering, joining, and mutating data. Ensures that mutations and changes to the graph are performed in place, streamlining your workflow for optimal productivity.

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

This contains functions and data used by the Open Visualization Academy classes on data processing and visualization. The tutorial included with this package requires the gradethis package which can be installed using "remotes::install_github('rstudio/gradethis')".

This package provides implementations of some of the most important outlier detection algorithms. Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) <doi:10.1002/widm.19>.

Growing collection of helper functions for point pattern analysis. Most functions are designed to work with the spatstat (<http://spatstat.org>) package. The focus of most functions are either null models or summary functions for spatial point patterns. For a detailed description of all null models and summary functions, see Wiegand and Moloney (2014, ISBN:9781420082548).

This package provides tools for the analysis of land use and cover (LUC) time series. It includes support for loading spatiotemporal raster data and synthesized spatial plotting. Several LUC change (LUCC) metrics in regular or irregular time intervals can be extracted and visualized through one- and multistep sankey and chord diagrams. A complete intensity analysis according to Aldwaik and Pontius (2012) <doi:10.1016/j.landurbplan.2012.02.010> is implemented, including tools for the generation of standardized multilevel output graphics.

Advanced forecasting algorithms for long-term energy demand at the national or regional level. The methodology is based on Grandón et al. (2024) <doi:10.1016/j.apenergy.2023.122249>; Zimmermann & Ziel (2024) <doi:10.1016/j.apenergy.2025.125444>. Real-time data, including power demand, weather conditions, and macroeconomic indicators, are provided through automated API integration with various institutions. The modular approach maintains transparency on the various model selection processes and encompasses the ability to be adapted to individual needs. oRaklE tries to help facilitating robust decision-making in energy management and planning.

An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the PhViD package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the mederrRank 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.

Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.

An implementation of the Blinder-Oaxaca decomposition for linear regression models.

Identify the optimal timing for new treatment initiation during multiple state disease transition, including multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) <doi:10.18637/jss.v038.i07>.

An unofficial wrapper for okx exchange v5 API <https://www.okx.com/docs-v5/en/>, including REST API and WebSocket API.

Allows you to easily execute expensive compute operations only once, and save the resulting object to disk.

Social media sites often embed cards when links are shared, based on metadata in the Open Graph Protocol (<https://ogp.me/>). This supports extracting that metadata from a website. It further allows for the creation of tags to add to a website to support the Open Graph Protocol and provides a list of the standard tags and their required properties.

Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor. If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions). Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change. This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.

Open the current working directory (or a given directory path) in your computer's file manager.

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.

This package provides a collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.