This package provides a model-agnostic safety layer for developers building with large language model (LLM) applications. Maps starter controls to the Open Worldwide Application Security Project Top 10 for Large Language Model Applications 2025 risk categories <https://genai.owasp.org/llm-top-10/> via a modular rule engine. Supports regular-expression rules, lightweight natural language processing (NLP) intent checks, optional scanners, and semantic large language model reviewer checks on prompts, conversations, retrieved context, tool inputs and outputs, streaming chunks, and model outputs. Supports workflows with the Ollama local web service <https://ollama.com/> via ellmer', remote reviewer endpoints, and other chat interfaces callable from R'. Intended as an experimental guardrail layer that teams should evaluate against their own workflows before relying on it in production.

Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.

This package provides a classification tree method that uses Uncorrelated Linear Discriminant Analysis (ULDA) for variable selection, split determination, and model fitting in terminal nodes. It automatically handles missing values and offers visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2410.23147>.

Build complex LaTeX mathematical expressions using intuitive R functions. Replace error-prone LaTeX syntax with readable, modular functions that make mathematical typesetting straightforward and maintainable.

LimeSurvey is Free/Libre Open Source Software for the development and administrations of online studies, using sophisticated tailoring capabilities to support multiple study designs (see <https://www.limesurvey.org>). This package supports programmatic creation of surveys that can then be imported into LimeSurvey', as well as user friendly import of responses from LimeSurvey studies.

Maximum likelihood estimation and likelihood ratio test are essential for modern statistics. This package supports in calculating likelihood based inference. Reference: Pawitan Y. (2001, ISBN:0-19-850765-8).

Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2019) <doi:10.1080/01621459.2018.1520115>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.

The solution of equality constrained least squares problem (LSE) is given through four analytics methods (Generalized QR Factorization, Lagrange Multipliers, Direct Elimination and Null Space method). We expose the orthogonal decomposition called Generalized QR Factorization (GQR) and also RQ factorization. Finally some codes for the solution of LSE applied in quaternions.

Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

This package provides tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. Broman et al. (2015) <doi:10.1534/g3.115.019778>.

This package implements a partitioned Empirical Bayes Expectation Conditional Maximization (ECM) algorithm for sparse high-dimensional linear mixed modeling as described in Zgodic, Bai, Zhang, and McLain (2025) <doi:10.1007/s11222-025-10649-z>. The package provides efficient estimation and inference for mixed models with high-dimensional fixed effects.

Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. Supported model families include Gaussian, logistic, Poisson, gamma, and inverse Gaussian regression. The package also provides cross-validation for selecting the penalty parameter.

This package provides a family of three complementary goodness-of-fit tests based on an adaptation of Hotelling's T-squared statistic applied to vectors of sample log-cumulants (Mellin statistics) for positive-support reliability data. The package provides the asymptotic chi-squared reference and parametric bootstrap p-values for reliable finite-sample inference, covering the Weibull, Frechet, Gamma, Inverse-Gamma, Log-Normal, and Log-Logistic families. It also provides three diagnostic diagrams (log-cumulant, kurtosis-skewness, and coefficient-of-variation) with bootstrap concentration ellipses, in the spirit of moment-ratio diagrams. Methods are described in Santos, Ospina, Espinheira and Oliveira (2025).

Identification of equilibrium locations in location games (Hotelling (1929) <doi:10.2307/2224214>). In these games, two competing actors place customer-serving units in two locations simultaneously. Customers make the decision to visit the location that is closest to them. The functions in this package include Prim algorithm (Prim (1957) <doi:10.1002/j.1538-7305.1957.tb01515.x>) to find the minimum spanning tree connecting all network vertices, an implementation of Dijkstra algorithm (Dijkstra (1959) <doi:10.1007/BF01386390>) to find the shortest distance and path between any two vertices, a self-developed algorithm using elimination of purely dominated strategies to find the equilibrium, and several plotting functions.

Set of the data science tools created by various members of the Long Term Ecological Research (LTER) community. These functions were initially written largely as standalone operations and have later been aggregated into this package.

These functions take a gene expression value matrix, a primary covariate vector, an additional known covariates matrix. A two stage analysis is applied to counter the effects of latent variables on the rankings of hypotheses. The estimation and adjustment of latent effects are proposed by Sun, Zhang and Owen (2011). "leapp" is developed in the context of microarray experiments, but may be used as a general tool for high throughput data sets where dependence may be involved.

This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.

In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect.

This package provides a function that, as an alternative to base::list, allows default values to be inherited from another list.

This package creates shareable links for R code in WebAssembly (WASM) Read-Eval-Print Loop (REPL) environments like webR <https://webr.r-wasm.org/> and for Shiny applications using Shinylive <https://shinylive.io/>. Supports single scripts, multi-file projects, exercise and solution pairs, and batch processing. Includes encoding, decoding, and previewing of links for both R and Python environments.

Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu).

Datasets and Functionality from Jan Beran (1994). Statistics for Long-Memory Processes; Chapman & Hall. Estimation of Hurst (and more) parameters for fractional Gaussian noise, fARIMA and FEXP models.

The Lorentz transformation in special relativity; also the gyrogroup structure of three-velocities. Performs active and passive transformations and has the ability to use units in which the speed of light is not unity. Includes some experimental functionality for celerity and rapidity. For general relativity, see the schwarzschild package.

Crabs in the English channel, deer skulls, English monarchs, half-caste Manga characters, Jamaican cities, Shakespeare's The Tempest, drugged up cyclists and sexually transmitted diseases.