This package produces quality scores for each of the US companies from the Russell 3000, following the approach described in "Quality Minus Junk" (Asness, Frazzini, & Pedersen, 2013) <http://www.aqr.com/library/working-papers/quality-minus-junk>. The package includes datasets for users who wish to view the most recently uploaded quality scores. It also provides tools to automatically gather relevant financials and stock price information, allowing users to update their data and customize their universe for further analysis.

This package provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, <doi:10.1007/s41237-024-00228-3>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for simulating various types of multistage designs.

Bindings to Uno (Unifying Nonlinear Optimization), a C++ solver for smooth nonlinearly constrained optimization. Uno unifies Lagrange-Newton methods, including sequential quadratic programming and interior-point methods, by decomposing them into interacting building blocks (constraint-relaxation, inequality-handling, Hessian, and globalization strategies) that can be freely combined, either through options or through presets that reproduce established solvers such as filterSQP and IPOPT'. The framework is described in Vanaret and Leyffer (2026) <doi:10.1007/s12532-026-00310-9>.

Updated versions of the 1970s "US State Facts and Figures" objects from the datasets package included with R. The new data is compiled from a number of sources, primarily from the United States Census Bureau or the relevant federal agency. Modern tidy tibbles provide richer state-level data including identifiers, geography, capitals, demographics, and socioeconomic statistics. Convenience vectors parallel the base datasets state objects but extend coverage to all 51 jurisdictions: the 50 states and the District of Columbia.

Extract and process bird sightings records from eBird (<http://ebird.org>), an online tool for recording bird observations. Public access to the full eBird database is via the eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download> for access), a downloadable text file. This package is an interface to AWK for extracting data from the EBD based on taxonomic, spatial, or temporal filters, to produce a manageable file size that can be imported into R.

Under natural conditions, nest temperatures fluctuate daily around a mean value, whereas in captivity they are often held constant. The Constant Temperature Equivalent is designed to bridge the gap between the two by calculating a single temperature value for wild nests that corresponds with the amount of development that would occur in an incubator set to the same temperature. The theory and formulas behind this method were developed by Professor Author Georges and are implemented here as a single function.

This package provides a collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see <https://www.ddaproject.com>.

Supports the analysis of Oceanographic data, including ADCP measurements, measurements made with argo floats, CTD measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the UNESCO or TEOS-10 equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.

Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>. Hoang H. Nguyen et al. (2015) <doi:10.3390/s150510481>. After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression.

This package implements the dynamic panel models described by Allison, Williams, and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. This class of models uses structural equation modeling to specify dynamic (lagged dependent variable) models with fixed effects for panel data. Additionally, models may have predictors that are only weakly exogenous, i.e., are affected by prior values of the dependent variable. Options also allow for random effects, dropping the lagged dependent variable, and a number of other specification choices.

Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) <doi:10.18637/jss.v053.i03>). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap.

This package provides a native R interface to agentic coding assistants. Talks directly to local command line interfaces ('GitHub Copilot', Anthropic Claude Code') over standard input/output and to a local vscode.lm bridge inside Positron', providing multi-model chat, tool calling, and in-session R evaluation. Supports a single stateful hal() session as well as disposable hal_ask() / hal_do() pipeline verbs, plot capture so models can see graphics, verified data transforms, and user-defined tools.

Reproducible work requires a record of where every statistic originated. When writing reports, some data is too big to load in the same environment and some statistics take a while to compute. This package offers a way to keep notes on statistics, simple functions, and small objects. Notepads can be locked to avoid accidental updates. Notepads keep track of who added the notes and when the notes were added. A simple text representation is used to allow for clear version histories.

Flexible and informed regression with Multiple Change Points. mcp can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and posterior predictive intervals are supported - also near the change points. mcp supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and PSIS-LOO model comparison. The mcp framework is introduced in Lindeløv (2020) <doi:10.31219/osf.io/fzqxv>.

This package provides tools for cleaning, processing, and preparing microbiome sequencing data (e.g., 16S rRNA) for downstream analysis. Supports CSV, TXT, and Excel file formats. The main function, ezclean(), automates microbiome data transformation, including format validation, transposition, numeric conversion, and metadata integration. It also handles taxonomic levels efficiently, resolves duplicated taxa entries, and outputs a well-structured, analysis-ready dataset. The companion functions ezstat() run statistical tests and summarize results, while ezviz() produces publication-ready visualizations.

This package provides functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.

Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length) and RL quantiles. Setting up control charts for given in-control ARL. The control charts under consideration are one- and two-sided EWMA, CUSUM, and Shiryaev-Roberts schemes for monitoring the mean or variance of normally distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added. Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided.

Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.

HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6.

This package provides an implementation of the product partition model described in Barry and Hartigan (2019) <doi:10.2307/2290726> for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) <doi:10.48550/arXiv.1509.00817>, allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented.

The Bayesian Federated Inference ('BFI') method combines inference results obtained from local data sets in the separate centers. In this version of the package, the BFI methodology is programmed for linear, logistic and survival regression models. For GLMs, see Jonker, Pazira and Coolen (2024) <doi:10.1002/sim.10072>; for survival models, see Pazira, Massa, Weijers, Coolen and Jonker (2026) <doi:10.1080/02664763.2025.2511932>; and for heterogeneous populations, see Jonker, Pazira and Coolen (2025) <doi:10.1017/rsm.2025.6>.

Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into brms syntax, a powerful and flexible framework for fitting Bayesian regression models using Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2025) <doi:10.3758/s13428-025-02643-0>.

In the context of data quality assessment, this package provides a number of functions for evaluating data quality across various dimensions, including completeness, plausibility, concordance, conformance, currency, timeliness, and correctness. It has been developed based on two well-known frameworksâ Michael G. Kahn (2016) <doi:10.13063/2327-9214.1244> and Nicole G. Weiskopf (2017) <doi:10.5334/egems.218>â for data quality assessment. Using this package, users can evaluate the quality of their datasets, provided that corresponding metadata are available.

This package contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. It includes several practical applications, such as the (deepAFT) model, which utilizes a deep neural network approach to enhance the accelerated failure time (AFT) model for survival data. Another example is the (deepGLM) model that applies deep neural network to the generalized linear model (glm), accommodating data types with continuous, categorical and Poisson distributions.