Three-step regression and inference for cellwise and casewise contamination.

Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the libLBFGS optimization library by Naoaki Okazaki.

Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

Offers a suite of tools designed to enhance the responsiveness and interactivity of web-based documents and applications created with R. It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). Furthermore, it offers an elegant image viewer and resizer (shinyExpandImage) perfect for interactive galleries. r2resize is particularly well-suited for developers and data scientists looking to create modern, responsive, and user-friendly shiny applications, markdown reports, and quarto documents that adapt gracefully to different screen sizes and user preferences, significantly improving the user experience.

Encode network data as strings of printable ASCII characters. Implemented functions include encoding and decoding adjacency matrices, edgelists, igraph, and network objects to/from formats graph6', sparse6', and digraph6'. The formats and methods are described in McKay, B.D. and Piperno, A (2014) <doi:10.1016/j.jsc.2013.09.003>.

This package provides a tool for mass deployment of shiny apps to RStudio Connect or Shiny Server'. Multiple user accounts and servers can be configured for deployment.

This package provides functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.

Predicts statistics of a reference distribution from a mixture of raw clinical measurements (healthy and pathological). Uses pretrained CNN models to estimate the mean, standard deviation, and reference fraction from 1D or 2D sample data. Methods are described in LeBien, Velev, and Roche-Lima (2026) "RINet: synthetic data training for indirect estimation of clinical reference distributions" <doi:10.1016/j.jbi.2026.104980>.

This package provides fast implementations of Random Forests, Gradient Boosting, and Linear Random Forests, with an emphasis on inference and interpretability. Additionally contains methods for variable importance, out-of-bag prediction, regression monotonicity, and several methods for missing data imputation.

Features the multiple polynomial quadratic sieve (MPQS) algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. The MPQS is based off of the seminal work of Carl Pomerance (1984) <doi:10.1007/3-540-39757-4_17> along with the modification of multiple polynomials introduced by Peter Montgomery and J. Davis as outlined by Robert D. Silverman (1987) <doi:10.1090/S0025-5718-1987-0866119-8>. Utilizes the C library GMP (GNU Multiple Precision Arithmetic). For smaller integers, a simple Elliptic Curve algorithm is attempted followed by a constrained version of Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm used by the factorize function in the gmp package.

Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, <doi:10.1016/j.neuroimage.2020.117341>). Supports brain imaging data structure (BIDS) <https://bids.neuroimaging.io> and native file structure to ingest signals from Matlab data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and FreeSurfer formats, providing brain imaging normalization to template brain, facilitating threeBrain package for comprehensive electrode localization via YAEL (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>).

The parametric Bayes analysis for the restricted mean survival time (RMST) with cluster effect, as described in Hanada and Kojima (2024) <doi:10.48550/arXiv.2406.06071>. Bayes estimation with random-effect and frailty-effect can be applied to several parametric models useful in survival time analysis. The RMST under these parametric models can be computed from the obtained posterior samples.

Aggregates multiple Receiver Operating Characteristic (ROC) curves obtained from different sources into one global ROC. Additionally, itâ s also possible to calculate the aggregated precision-recall (PR) curve.

We provide functions to perform an empirical small telescopes analysis. This package contains 2 functions, SmallTelescopes() and EstimatePower(). Users only need to call SmallTelescopes() to conduct the analysis. For more information on small telescopes analysis see Uri Simonsohn (2015) <doi:10.1177/0956797614567341>.

This package provides a machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.

Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" <doi:10.1007/BF00116466>, Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" <doi:10.1093/biostatistics/4.1.11>, Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" <doi:10.1214/17-AOAS1068>, and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" <doi:10.1016/j.sste.2021.100420>.

Formats for R Markdown that undo modifications by pandoc and rmarkdown to original latex templates, such as smaller margins, paragraph spacing, and compact titles. In addition, enhancements such as author blocks with affiliations and headers and footers are introduced. All of this functionality is built around plugins that modify the default pandoc template without relying on custom templates.

This package provides a collection of tools to import and structure the (currently) single-season event, game-log, roster, and schedule data available from <https://www.retrosheet.org>. In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse. This package does the parsing on those files, returning the requested data in the most practical R structure to use for sabermetric or other analyses.

This package provides a collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. Given a large collection of measurement units, the r-value, r, of a particular unit is a reported percentile that may be interpreted as the smallest percentile at which the unit should be placed in the top r-fraction of units.

This package provides a framework that supports creating and extending enterprise Shiny applications using best practices.

This package provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.

Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents <https://academictorrents.com/collection/mvl-datasets>.

This package provides functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be quantified by counts of categories, measures of mass or length, or proportions. Monte Carlo resampling procedures are available for hypothesis testing against multinomial null models. Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079> and associated references.