Extra family objects in "weird" scenarios, particularly logistic or log-linear model with unbounded or non-binary/non-integer outcomes. Provides binomial_extra() and poisson_extra() as generalizations of binomial() and poisson(). The use of canonical link with the corresponding working likelihood in glm() ensures convexity, making model fitting reliable and independent of starting value. Robert WM Wedderburn (1974) <doi:10.1093/biomet/61.3.439> and Peter McCullagh (1983) <doi:10.1214/aos/1176346056> justified this method to fit generalized linear (mean) models with quasi-/working likelihood.

Calculates the co-ranking matrix to assess the quality of a dimensionality reduction.

This package provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.

Data recorded as paths or trajectories may be suitably described by curves, which are independent of their parametrization. For the space of such curves, the package provides functionalities for reading curves, sampling points on curves, calculating distance between curves and for computing Tukey curve depth of a curve w.r.t. to a bundle of curves. For details see Lafaye De Micheaux, Mozharovskyi, and Vimond (2021) <doi:10.48550/arXiv.1901.00180>.

This package provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

This package implements cointegration tests with structural breaks designed for small sample sizes, following the methodology of Trinh (2022) <https://ideas.repec.org/p/ema/worpap/2022-01.html>. Supports models with no breaks, breaks in constant only, and breaks in both constant and slope. Provides endogenous break date detection using ADF or SSR minimization criteria, with small-sample adjusted critical values via response surface methodology.

Extracts colors from various image types, returns customized reports and plots treemaps and 3D scatterplots of image compositions. Color palettes can also be created.

Concordance probability estimate (CPE) is a commonly used performance measure in survival analysis that evaluates the predictive accuracy of a survival model. It measures how well a model can distinguish between pairs of individuals with different survival times. Specifically, it calculate the proportion of all pairs of individuals whose predicted survival times are correctly ordered.

Given a non-linear model, calculate the local explanation. We purpose view the data space, explanation space, and model residuals as ensemble graphic interactive on a shiny application. After an observation of interest is identified, the normalized variable importance of the local explanation is used as a 1D projection basis. The support of the local explanation is then explored by changing the basis with the use of the radial tour <doi:10.32614/RJ-2020-027>; <doi:10.1080/10618600.1997.10474754>.

This package provides a simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. For achieving this, a virtual forest estate area is split into the areas covered by typical phases of the silvicultural concept of interest. Given initial area shares of these phases, the dynamics of these areas is simulated. The typical carbon stocks and flows which are known for all phases are attributed post-hoc to the areas and upscaled to the estate level. CO2 emissions by forest operations are estimated based on the amounts and dimensions of the harvested timber. Probabilities of damage events are taken into account.

This package creates auto-grading check-fields and check-boxes for rmarkdown or quarto HTML. It can be used in class, when teacher share materials and tasks, so students can solve some problems and check their work. In contrast to the learnr package, the checkdown package works serverlessly without shiny'.

This package provides an interactive shiny web application for constructing, analyzing, and visualizing composite indices from multidimensional datasets. Users can upload or select indicator data, group variables into logical categories, apply normalization and weighting methods (such as equal or custom schemes), and compute aggregate composite indices. The shiny interface includes tools for exploring results through tables, plots, and data exports, making it useful for researchers, policymakers, and analysts interested in index-based evaluations.

This package performs analysis of complex dynamic systems with a focus on the temporal unfolding of patterns, changes, and state transitions in behavioral data. Supports both time series and sequence data and provides tools for the analysis and visualization of complexity, pattern identification, trends, regimes, sequence typology as well as early warning signals.

An agent runtime that gives Large Language Models (LLMs) from Anthropic <https://www.anthropic.com/>, OpenAI <https://openai.com/>, Moonshot <https://www.moonshot.ai/>, and Ollama <https://ollama.com/> direct access to a live R session with managed workspace state. Tools execute as R function calls with provenance tracking, and a deterministic retrieval system keeps relevant objects in context across turns. Three entry points: a shell command-line interface (CLI), a console read-eval-print-loop via chat(), and a Model Context Protocol (MCP) server via serve() for external clients.

This package contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.

This package provides a unified workflow for running, classifying, visualizing, and interpreting sensitivity analyses for unmeasured confounding across multiple causal frameworks. Introduces the Causal Fragility Index (CFI), a single 0-100 composite score that integrates evidence from the partial R-squared robustness value approach (Cinelli and Hazlett, 2020, <doi:10.1111/rssb.12348>), E-value metrics (VanderWeele and Ding, 2017, <doi:10.7326/M16-2607>), and the Impact Threshold for a Confounding Variable (Frank, 2000, <doi:10.1177/0049124100029002001>) into one interpretable measure of robustness. The package also provides template-based plain-language narrative interpretation and publication-ready reporting, with optional integration with the confoundvis package for sensitivity plots.

Computes the cosine-correlation coefficient for measuring the degree of linear dependence among variables in a multidimensional context. The package implements the generalized cosine-correlation theorem for p-1 variables, providing a quantitative assessment of interrelationships within experimental frameworks. This methodology extends classical correlation measures to higher-dimensional spaces using a dimensional exploration approach based on time scale calculus.

Analyze data from a crossover design using generalized estimation equations (GEE), including carryover effects and various correlation structures based on the Kronecker product. It contains functions for semiparametric estimates of carry-over effects in repeated measures and allows estimation of complex carry-over effects. Related work includes: a) Cruz N.A., Melo O.O., Martinez C.A. (2023). "CrossCarry: An R package for the analysis of data from a crossover design with GEE". <doi:10.48550/arXiv.2304.02440>. b) Cruz N.A., Melo O.O., Martinez C.A. (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures". <doi:10.1007/s00362-022-01391-z> and c) Cruz N.A., Melo O.O., Martinez C.A. (2023). "Semiparametric generalized estimating equations for repeated measurements in cross-over designs". <doi:10.1177/09622802231158736>.

An implementation of the statistical methods commonly used for advanced composite materials in aerospace applications. This package focuses on calculating basis values (lower tolerance bounds) for material strength properties, as well as performing the associated diagnostic tests. This package provides functions for calculating basis values assuming several different distributions, as well as providing functions for non-parametric methods of computing basis values. Functions are also provided for testing the hypothesis that there is no difference between strength and modulus data from an alternate sample and that from a "qualification" or "baseline" sample. For a discussion of these statistical methods and their use, see the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4). Additional details about this package are available in the paper by Kloppenborg (2020, <doi:10.21105/joss.02265>).

Implement tableGrob object as a clickable image map. The clickableImageMap package is designed to be more convenient and more configurable than the edit() function. Limitations that I have encountered with edit() are cannot control (1) positioning (2) size (3) appearance and formatting of fonts In contrast, when the table is implemented as a tableGrob', all of these features are controllable. In particular, the ggplot2 grid system allows exact positioning of the table relative to other graphics etc.

Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.

Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.

This package provides a tidied subset of the US College Scorecard dataset, containing institutional characteristics, enrollment, student aid, costs, and student outcomes at institutions of higher education in the United States.

Generate mean and median weighted or unweighted spatial centers. Functions are analogous to their identically named counterparts within ArcGIS Pro'. Median center methodology based off of Kuhn and Kuenne (1962) <doi:10.1111/j.1467-9787.1962.tb00902.x>.