This package provides tools for common operations on lists. Provided are short-cuts to operations like selecting and merging data stored in lists. The functions in this package are designed to be used with pipes.

Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical methods rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. In the so-called "leaky" IV setting, candidate instruments are allowed to have some direct influence on outcomes, rendering the average treatment effect (ATE) unidentifiable. But with limits on the amount of information leakage, we may still recover sharp bounds on the ATE, providing partial identification. This package implements methods for ATE bounding in the leaky IV setting with linear structural equations. For details, see Watson et al. (2024) <doi:10.48550/arXiv.2404.04446>.

Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.

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 a system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).

Create maps made of lines. The package contains one function: linemap(). linemap() displays a map made of lines using a raster or gridded data.

Testing differential abundance at individual taxa and in a whole microbial community. The tests are based on the log-ratio of relative abundances. The tests accommodate continuous, discrete (binary, categorical), and multivariate traits, and allow adjustment of confounders. For more details see He (2026) <doi:10.64898/2026.04.07.716976>.

Translates R help documentation on the fly by using a Large Language model of your choice. If you are using RStudio or Positron the translated help will appear in the help pane.

This package provides a framework that allows for easy logging of changes in data. Main features: start tracking changes by adding a single line of code to an existing script. Track changes in multiple datasets, using multiple loggers. Add custom-built loggers or use loggers offered by other packages. <doi:10.18637/jss.v098.i01>.

Apply Univariate Long Memory Models, Apply Multivariate Short Memory Models To Hydrological Dataset, Estimate Intensity Duration Frequency curve to rainfall series. NEW -- Calculate the monthly water requirement for herbaceous and arboreal plants.

This package provides R bindings to decode DAG-CBOR (Directed Acyclic Graph Concise Binary Object Representation) encoded data, CIDs (Content Identifiers), and CAR (Content Addressable aRchive) files using the Rust IPLD (InterPlanetary Linked Data) library <https://github.com/ipld/libipld>. This is especially useful for working with data from IPFS (InterPlanetary File System) and AtProto (Bluesky) applications.

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).

This package provides an interactive shiny application to construct stage-structured life tables from tabular input data. The application includes input validation, demographic calculations, visualization tools, and export of tables and figures to support reproducible workflows in ecological and entomological studies. Methods for life table construction follow classical demographic approaches described in Martinez (2015) <doi:10.13140/RG.2.2.21333.65760>.

This package provides the tables from the Sean Lahman Baseball Database as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2024, as recorded in the 2025 version of the database. Documentation examples show how many baseball questions can be investigated.

Plots path diagrams from models in lavaan using the plotting functionality from the DiagrammeR package. DiagrammeR provides nice path diagrams via Graphviz', and these functions make it easy to generate these diagrams from a lavaan path model without having to write the DOT language graph specification.

This package provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

Flexible procedures to compute local density-based outlier scores for ranking outliers. Both exact and approximate nearest neighbor search can be implemented, while also accommodating multiple neighborhood sizes and four different local density-based methods. It allows for referencing a random subsample of the input data or a user specified reference data set to compute outlier scores against, so both unsupervised and semi-supervised outlier detection can be implemented.

This package provides a collection of large language model (LLM) text analysis methods designed with psychological data in mind. Currently, LLMing (aka "lemming") includes a text anomaly detection method based on the angle-based subspace approach described by Zhang, Lin, and Karim (2015) and a text generation method. <doi:10.1016/j.ress.2015.05.025>.

This package provides tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2020a, <doi:10.48550/arXiv.1804.04916>) and Cattaneo, Farrell and Feng (2020b, <doi:10.48550/arXiv.1906.00202>): lsprobust() for nonparametric point estimation of regression functions and their derivatives and for robust bias-corrected (pointwise and uniform) inference; lspkselect() for data-driven selection of the IMSE-optimal number of knots; lsprobust.plot() for regression plots with robust confidence intervals and confidence bands; lsplincom() for estimation and inference for linear combinations of regression functions from different groups.

This package implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.

Life and Fertility Tables are appropriate to study the dynamics of arthropods populations. This package provides utilities for constructing Life Tables and Fertility Tables, related demographic parameters, and some simple graphs of interest. It also offers functions to transform the obtained data into a known format for better manipulation. In addition, two methods for obtaining the confidence interval are included.

Interactive shiny application for working with different kinds of latent variable analysis, with the lavaan package. Graphical output for models are provided and different estimators are supported.

Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are logConDiscrMLE that allows computation of the log-concave MLE, logConDiscrCI that computes pointwise confidence bands for the MLE, and kInflatedLogConDiscr that computes a mixture of a log-concave PMF and a point mass at k.

Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. LKT is a method to compute features of student data that are used as predictors of subsequent performance. LKT allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of LiblineaR', which enables extremely fast solutions compared to base glm() in R.