Estimates sugar beet canopy closure with remotely sensed leaf area index and estimates when action might be needed to protect the crop from a Leaf Spot epidemic with a negative prognosis model based on published models.

This package provides a header only, C++ interface to R with enhancements over cpp11'. Enforces copy-on-write semantics consistent with R behavior. Offers native support for ALTREP objects, UTF-8 string handling, modern C++ features and idioms, and reduced memory requirements. Allows for vendoring, making it useful for restricted environments. Compared to cpp11', it adds support for converting C++ maps to R lists, Roxygen documentation directly in C++ code, proper handling of matrix attributes, support for nullable external pointers, bidirectional copy of complex number types, flexibility in type conversions, use of nullable pointers, and various performance optimizations.

Create CUSUM (cumulative sum) statistics from a vector or dataframe. Also create single or faceted CUSUM control charts, with or without control limits. Accepts vector, dataframe, tibble or data.table inputs.

This package provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns mle objects compatible with algebraic.mle for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" <doi:10.1007/978-0-387-40065-5>.

This package provides tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Patkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The plot() and summary() methods are implemented for the results returned by cumulcalib().

Filtering, also known as gating, of flow cytometry samples using the curvHDR method, which is described in Naumann, U., Luta, G. and Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.

Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.

Clique percolation community detection for weighted and unweighted networks as well as threshold and plotting functions. For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180> and Palla et al. (2005) <doi:10.1038/nature03607>.

This package provides a GUI with which users can construct and interact with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.

Browser cookies are name-value pairs that are saved in a user's browser by a website. Cookies allow websites to persist information about the user and their use of the website. Here we provide tools for working with cookies in shiny apps, in part by wrapping the js-cookie JavaScript library <https://github.com/js-cookie/js-cookie>.

This package performs forward and backward stepwise regression for the proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events. This version includes improved handling of factors, interactions, and polynomial terms.

Estimation of 2-level factor copula-based regression models for clustered data where the response variable can be either discrete or continuous.

This package implements collaborative filtering methods for recommendation systems based on user-item interaction data. Supports both explicit feedback (ratings) and implicit feedback (consumption). The package uses efficient sparse matrix representations and provides incremental updates for users, items, and similarity structures through an R6 class-based architecture. See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.

This package provides a method for pattern discovery in weighted graphs as outlined in Thistlethwaite et al. (2021) <doi:10.1371/journal.pcbi.1008550>. Two use cases are achieved: 1) Given a weighted graph and a subset of its nodes, do the nodes show significant connectedness? 2) Given a weighted graph and two subsets of its nodes, are the subsets close neighbors or distant?

Create cumulative odds ratio plot to visually inspect the proportional odds assumption from the proportional odds model.

The Certifiably Optimal RulE ListS (Corels) learner by Angelino et al described in <doi:10.48550/arXiv.1704.01701> provides interpretable decision rules with an optimality guarantee, and is made available to R with this package. See the file AUTHORS for a list of copyright holders and contributors.

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The crew.cluster package extends the mirai'-powered crew package with worker launcher plugins for traditional high-performance computing systems. Inspiration also comes from packages mirai by Gao (2023) <https://github.com/r-lib/mirai>, future by Bengtsson (2021) <doi:10.32614/RJ-2021-048>, rrq by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>, clustermq by Schubert (2019) <doi:10.1093/bioinformatics/btz284>), and batchtools by Lang, Bischl, and Surmann (2017). <doi:10.21105/joss.00135>.

Deriving skill structures from skill assignment data for courses (sets of learning objects).

This package contains functions for estimating generalized parametric mixture and non-mixture cure models <doi:10.1016/j.cmpb.2022.107125>, loss of lifetime, mean residual lifetime, and crude event probabilities.

Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <doi:10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns cross-sectional correlation, autocorrelation, and volatility clustering without power loss.

This package provides object-oriented database management tools for working with large datasets across multiple database systems. Features include robust connection management for PostgreSQL databases, advanced table operations with bulk data loading and upsert functionality, comprehensive data validation through customizable field type and content validators, efficient index management, and cross-database compatibility. Designed for high-performance data operations in surveillance systems and large-scale data processing workflows.

This package implements the CKNNRLD algorithm (Clustering-Based K-Nearest Neighbor Regression for Longitudinal Data) for improving K-Nearest Neighbor ('KNN') regression on longitudinal data through cluster-based partitioning and localized prediction. Offers enhanced computational efficiency and accuracy for high-volume longitudinal datasets. The acronym KNN stands for K-Nearest Neighbor. References: Loeloe MS, Tabatabaei SM, Sefidkar R, Mehrparvar AH, Jambarsang S (2025). "Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach." BMC Bioinformatics, 26, 232. <doi:10.1186/s12859-025-06205-1>.

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.

Building on top of the RcppArmadillo linear algebra functionalities to do fast spatial interaction models in the context of urban analytics, geography, transport modelling. It uses the Newton root search algorithm to determine the optimal cost exponent and can run country level models with thousands of origins and destinations. It aims at implementing an easy approach based on matrices, that can originate from various routing and processing steps earlier in an workflow. Currently, the simplest form of production, destination and doubly constrained models are implemented. Schlosser et al. (2023) <doi:10.48550/arXiv.2309.02112>.