Scraping content from archived web pages stored in the Internet Archive (<https://archive.org>) using a systematic workflow. Get an overview of the mementos available from the respective homepage, retrieve the Urls and links of the page and finally scrape the content. The final output is stored in tibbles, which can be then easily used for further analysis.

This package provides statistical auditing, risk documentation, and reporting tools to support AI governance workflows for employment and hiring decision systems. Implements the EEOC four-fifths adverse impact rule (Equal Employment Opportunity Commission, 1978, <https://www.ecfr.gov/current/title-29/subtitle-B/chapter-XIV/part-1607>), NYC Local Law 144 bias audit requirements (New York City, 2023, <https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page>), and the AI Risk Management Framework checklist items from the National Institute of Standards and Technology (2023, <doi:10.6028/NIST.AI.100-1>). Optionally supports EU AI Act high-risk classification (European Parliament and Council, 2024, <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689>). The package does not provide legal advice or certify legal compliance; it is a statistical and documentation support tool.

This package implements the framework of Tiwari and Majumdar (2025) <doi:10.48550/arXiv.2512.07154> for valuing arithmetic and geometric Asian options under transient and permanent market impact. Provides three pricing approaches: Kemna-Vorst frictionless benchmarks, exogenous diffusion pricing (closed-form for geometric, Monte Carlo for arithmetic), and endogenous Hamilton-Jacobi-Bellman valuation via a tree-based Bellman scheme producing indifference bid-ask prices.

It can sometimes be difficult to ascertain when some events (such as property crime) occur because the victim is not present when the crime happens. As a result, police databases often record a start (or from') date and time, and an end (or to') date and time. The time span between these date/times can be minutes, hours, or sometimes days, hence the term Aoristic'. Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. For events with a location described by either a latitude/longitude or X/Y coordinate pair, and a start and end date/time, this package generates an aoristic data frame with aoristic weighted probability values for each hour of the week, for each observation. The coordinates are not necessary for the program to calculate aoristic weights; however, they are part of this package because a spatial component has been integral to aoristic analysis from the start. Dummy coordinates can be introduced if the user only has temporal data. Outputs include an aoristic data frame, as well as summary graphs and displays. For more information see: Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, Journal of Quantitative Criminology. 18 (1): 23-43. Note: This package replaces an original aoristic package (version 0.6) by George Kikuchi that has been discontinued with his permission.

Allows users to stem Arabic texts for text analysis.

Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.

R and C++ functions to perform exact and approximate optimal transport. All C++ methods can be linked to other R packages via their header files.

Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes.

The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.

This package provides a comprehensive suite of statistical and analytical tools for agricultural research. Includes complete analysis of variance (ANOVA) functions for all experimental designs: Completely Randomized Design (CRD), Randomized Block Design (RBD), Pooled RBD, Split Plot with all variations, Split-Split Plot, Strip Plot, Latin Square, Factorial, Augmented, and Alpha Lattice, with proper error terms and comprehensive Standard Error (SE) and Critical Difference (CD) calculations. Features multiple post-hoc tests: Least Significant Difference (LSD), Duncan Multiple Range Test (DMRT), Tukey Honestly Significant Difference (HSD), Student-Newman-Keuls (SNK), Scheffe, Bonferroni, and Dunnett, along with assumption checking and publication-ready output. Advanced methods include stability analysis using Eberhart-Russell regression, Additive Main Effects and Multiplicative Interaction (AMMI), Finlay-Wilkinson regression, Shukla stability variance, Wricke ecovalence, Coefficient of Variation (CV), and Cultivar Superiority Index as described in Eberhart and Russell (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. Thermal indices include Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), and Heat Use Efficiency (HUE). Crop growth analysis covers Crop Growth Rate (CGR), Relative Growth Rate (RGR), Net Assimilation Rate (NAR), and Leaf Area Index (LAI). Also provides harvest index, yield gap analysis, economic efficiency indices (Benefit-Cost ratio), nutrient use efficiency calculations, correlation matrix, Principal Component Analysis (PCA), path analysis, and Structural Equation Modeling (SEM). Statistical methods follow Gomez and Gomez (1984, ISBN:0471870927) and Panse and Sukhatme (1985, ISBN:8170271169).

The ggarrow package is a ggplot2 extension that plots a variety of different arrow segments with many options to customize. The arrowheadr package makes it easy to create custom arrowheads and fins within the parameters that ggarrow functions expect. It has preset arrowheads and a collection of functions to create and transform data for customizing arrows.

This package provides the infrastructure for association rule-based classification including the algorithms CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.

Specification, review, and scaffolding helpers for AI agent systems. The package standardizes workflow, memory, knowledge, interface, proposal, and review artifacts so humans and coding assistants can infer, inspect, revise, and hand off task designs. It intentionally excludes communication layers, provider-specific model client code, and full runtime execution engines so that design artifacts and implementation transport remain cleanly separated.

This package implements the alpha-power hazard model and regression frameworks for survival data based on the flexible hazard rate function h(x; alpha, beta) = alpha^x + x^(beta-1) (Pal et al., 2026 <doi:10.1007/s41096-026-00297-5>). Provides standard distribution functions (d, p, q, r, h, H, s) and distributional properties including raw/central moments, variance, skewness, kurtosis, quantile statistics (Bowley's skewness, Moors's kurtosis), Lambert W hazard rate function minimum (Corless et al., 1996), order statistics, and stochastic ordering (Shaked & Shanthikumar, 1994). Computes five classical estimation methods for baseline parameters: Maximum Likelihood Estimation (Casella & Berger, 2002), Least Squares Estimation (Swain et al., 1988), Weighted Least Squares Estimation (Styan, 1973), Maximum Product of Spacings Estimation (Cheng & Amin, 1983 <doi:10.1111/j.2517-6161.1983.tb01241.x>), and Cramer-von Mises Estimation (Macdonald, 1971). Supports four hazard regression models (M1-M4) within proportional hazards and parametric frameworks across uncensored data, right censoring, left censoring, interval censoring, and progressive Type-I and Type-II censoring schemes (Lee & Wang, 2003; Lawless, 2011; Balakrishnan & Aggarwala, 2000). Includes comprehensive model diagnostics, Cox-Snell, martingale, deviance, standardized, and studentized residuals, leverage, Cook's distance, DFFITS, DFBETAS, model comparisons (AIC, BIC, WAIC), k-fold cross-validation, prediction suites, random data generators, and an eight-panel diagnostic visualization suite.

This package creates pre- and post- intervention scattergrams based on audiometric data. These scattergrams are formatted for publication in Otology & Neurotology and other otolaryngology journals. For more details and instructions on how to use this package, please reference Pan and Oghalai (2026) <doi: 10.1097/MAO.0000000000004693>. For further history on the development of scattergrams for reporting audiometric results in clinical trials, please see Gurgel et al (2012) <doi:10.1177/0194599812458401>, Oghalai and Jackler (2016) <doi:10.1177/0194599816638314>.

This package provides functions to perform global (genome-wide) and local admixture inference from bi- and multi-allelic marker dosages (discrete or continuous) in polyploid species.

Create and evaluate models using tidymodels and h2o <https://h2o.ai/>. The package enables users to specify h2o as an engine for several modeling methods.

This package provides functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes were the most used and copied set of standards in the world. They are intended to be used for sampling a stream of lots, and were used in contract agreements between supplier and customer companies. When the US military dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This package is useful because its computer implementation of these tables duplicates that available in other commercial software and subscription online calculators.

Builds and analyses Policy Analysis Matrices ('PAMs') for agricultural production systems. Computes private and social profitability, policy transfers, the domestic resource cost ratio, nominal protection coefficients for outputs and inputs, the effective protection coefficient, the private cost ratio, the profitability coefficient, subsidy ratios, and social cost-benefit ratios. Supports itemised farm budgets, parity prices, grouped analysis, deterministic sensitivity analysis, switching values, and correlated Monte Carlo simulation. Methods follow Monke and Pearson (1989, ISBN:0801419530) and the Food and Agriculture Organization of the United Nations (2007, ISBN:9789251057476).

Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.

Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via lme4'), nonlinear growth curves (logistic, Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) <doi:10.1371/journal.pone.0146021>, and Bates and others (2015) <doi:10.18637/jss.v067.i01>.

Analysis of data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) <doi:10.1198/004017004000000608>.

This package provides a collection of tools for the analysis of habitat selection.

Package ACV (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>.