This package implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.

This package provides API access to the Government of Canada Vehicle Recalls Database <https://tc.api.canada.ca/en/detail?api=VRDB> used by the Defect Investigations and Recalls Division for vehicles, tires, and child car seats. The API wrapper provides access to recall summary information searched using make, model, and year range, as well as detailed recall information searched using recall number.

This package provides tools to visualize the results of a classification or a regression. The graphical displays include stacked plots, silhouette plots, quasi residual plots, class maps, predictions plots, and predictions correlation plots. Implements the techniques described and illustrated in Raymaekers J., Rousseeuw P.J., Hubert M. (2022). Class maps for visualizing classification results. \emphTechnometrics, 64(2), 151â 165. \doi10.1080/00401706.2021.1927849 (open access), Raymaekers J., Rousseeuw P.J.(2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. \emphJournal of Computational and Graphical Statistics, 31(4), 1332â 1343. \doi10.1080/10618600.2022.2050249, and Rousseeuw, P.J. (2025). Explainable Linear and Generalized Linear Models by the Predictions Plot. <doi:10.48550/arXiv.2412.16980> (open access). Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", "Neural_net_examples", and "predsplot_examples".

This package contains some commonly used categorical variable encoders, such as LabelEncoder and OneHotEncoder'. Inspired by the encoders implemented in Python sklearn.preprocessing package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).

Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).

Simulate one or many frequentist confidence clinical trials based on a specified set of parameters. From a two-arm, single-stage trial to a perpetually run Adaptive Platform Trial, this package offers vast flexibility to customize your trial and observe operational characterisitics over thousands of instances.

For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, Kowal and Wu (2022) <doi:10.1111/biom.13617>, King and Kowal (2022) <arXiv:2110.14790>, and Kowal and Wu (2023) <arXiv:2110.12316>.

This package provides functions for computing the one-sided p-values of the Cochran-Armitage trend test statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.

This package contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.

Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination.

Process command line arguments, as part of a data analysis workflow. command makes it easier to construct a workflow consisting of lots of small, self-contained scripts, all run from a Makefile or shell script. The aim is a workflow that is modular, transparent, and reliable.

Converts numbers to continued fractions and back again. A solver for Pell's Equation is provided. The method for calculating roots in continued fraction form is provided without published attribution in such places as Professor Emeritus Jonathan Lubin, <http://www.math.brown.edu/jlubin/> and his post to StackOverflow, <https://math.stackexchange.com/questions/2215918> , or Professor Ron Knott, e.g., <https://r-knott.surrey.ac.uk/Fibonacci/cfINTRO.html> .

Designs guide sequences for CRISPR/Cas9 genome editing and provides information on sequence features pertinent to guide efficiency. Sequence features include annotated off-target predictions in a user-selected genome and a predicted efficiency score based on the model described in Doench et al. (2016) <doi:10.1038/nbt.3437>. Users are able to import additional genomes and genome annotation files to use when searching and annotating off-target hits. All guide sequences and off-target data can be generated through the R console with sgRNA_Design() or through crispRdesignR's user interface with crispRdesignRUI(). CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and the associated protein Cas9 refer to a technique used in genome editing.

ClickHouse (<https://clickhouse.com/>) is an open-source, high performance columnar OLAP (online analytical processing of queries) database management system for real-time analytics using SQL. This DBI backend relies on the ClickHouse HTTP interface and support HTTPS protocol.

This package provides methods and tools for performing multistep-ahead time series forecasting using conformal prediction methods including classical conformal prediction, adaptive conformal prediction, conformal PID (Proportional-Integral-Derivative) control, and autocorrelated multistep-ahead conformal prediction. The methods were described by Wang and Hyndman (2024) <doi:10.48550/arXiv.2410.13115>.

This package provides a tool that imports, subsets, and exports the CongressData dataset. CongressData contains approximately 800 variables concerning all US congressional districts with data back to 1789. The dataset tracks district characteristics, members of Congress, and the political behavior of those members. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, identify the citations associated with their searches, and more.

This package contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.

This package provides a genome-wide survival framework that integrates sequential conditional independent tuples and saddlepoint approximation method, to provide SNP-level false discovery rate control while improving power, particularly for biobank-scale survival analyses with low event rates. The method is based on model-X knockoffs as described in Barber and Candes (2015) <doi:10.1214/15-AOS1337> and fast survival analysis methods from Bi et al. (2020) <doi:10.1016/j.ajhg.2020.06.003>. A shrinkage algorithmic leveraging accelerates multiple knockoffs generation in large genetic cohorts. This CRAN version uses standard Cox regression for association testing. For enhanced performance on very large datasets, users may optionally install the SPACox package from GitHub which provides saddlepoint approximation methods for survival analysis.

Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.

This package provides tools to process CBASS-derived PAM data efficiently. Minimal requirements are PAM-based photosynthetic efficiency data (or data from any other continuous variable that changes with temperature, e.g. relative bleaching scores) from 4 coral samples (nubbins) subjected to 4 temperature profiles of at least 2 colonies from 1 coral species from 1 site. Please refer to the following CBASS (Coral Bleaching Automated Stress System) papers for in-depth information regarding CBASS acute thermal stress assays, experimental design considerations, and ED5/ED50/ED95 thermal parameters: Nicolas R. Evensen et al. (2023) <doi:10.1002/lom3.10555> Christian R. Voolstra et al. (2020) <doi:10.1111/gcb.15148> Christian R. Voolstra et al. (2025) <doi:10.1146/annurev-marine-032223-024511>.

This package provides tools for Delphi's COVIDcast Epidata API: data access, maps and time series plotting, and basic signal processing. The API includes a collection of numerous indicators relevant to the COVID-19 pandemic in the United States, including official reports, de-identified aggregated medical claims data, large-scale surveys of symptoms and public behavior, and mobility data, typically updated daily and at the county level. All data sources are documented at <https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html>.

Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable <doi:10.1007/s11336-020-09723-4>.

Function using lemmatization to classify educational programs according to the CINE(Classification International Normalized of Education) for Peru.

Monte Carlo simulation framework for different randomized clinical trial designs with a special emphasis on estimators based on covariate adjustment. The package implements regression-based covariate adjustment (Rosenblum & van der Laan (2010) <doi:10.2202/1557-4679.1138>) and a one-step estimator (Van Lancker et al (2024) <doi:10.48550/arXiv.2404.11150>) for trials with continuous, binary and count outcomes. The estimation of the minimum sample-size required to reach a specified statistical power for a given estimator uses bisection to find an initial rough estimate, followed by stochastic approximation (Robbins-Monro (1951) <doi:10.1214/aoms/1177729586>) to improve the estimate, and finally, a grid search to refine the estimate in the neighborhood of the current best solution.