This package provides tools to retrieve and summarize taxonomic information and synonymy data for reptile species using data scraped from The Reptile Database website (<https://reptile-database.reptarium.cz/>). Outputs include clean and structured data frames useful for ecological, evolutionary, and conservation research.

This package creates HTML strings to embed tables, images or graphs in pop-ups of interactive maps created with packages like leaflet or mapview'. Handles local images located on the file system or via remote URL. Handles graphs created with lattice or ggplot2 as well as interactive plots created with htmlwidgets'.

Collections of functions allowing random number generations and estimation of Liouville copulas, as described in Belzile and Neslehova (2017) <doi:10.1016/j.jmva.2017.05.008>.

Constructs likelihood models from heterogeneous observation types by composing named contributions. Each observation type (exact, left-censored, right-censored, interval-censored, or custom) contributes independently to the total log-likelihood, which is summed under an i.i.d. assumption. Provides contr_name() for standard R distributions and contr_fn() for user-defined contributions, composed via likelihood_contr() into objects compatible with the likelihood.model inference framework.

This package provides methods for linear regression in the presence of missing data, including missingness in covariates and responses. The package implements two estimators: oss_estimator(), a low-dimensional semi-supervised method, and dantzig_missing(), a high-dimensional approach. The tuning parameter can be selected automatically via cv_dantzig_missing(). See Risebrow and Berrett (2026) <doi:10.48550/arXiv.2602.13729>. Optional support for the gurobi optimizer via the gurobi R package (available from Gurobi, see <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html>).

An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.

This package provides a bioinformatics pipeline for performing taxonomic assignment of DNA metabarcoding sequence data while considering geographic location. A detailed tutorial is available at <https://urodelan.github.io/LocaTT_Tutorial/>. A manuscript describing these methods is in preparation.

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

Implementation based on Zhang, Jie & Huang, Kun (2014) <doi:10.4137/CIN.S14021> Normalized ImQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers. Cancer informatics, 13, CIN-S14021.

This package provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

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 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.

Lexical response data is a package that can be used for processing cued-recall, free-recall, and sentence responses from memory experiments.

Construct core collections using the information measure Length of Encoded Attribute Values (LEAV) using qualitative and/or quantitative trait data as described by Balakrishnan and Suresh (2001a) <https://indianjournals.com/article/ijpgr-14-1-006> and (2001b) <https://indianjournals.com/article/ijpgr-14-3-005>.

Back-end connections to LattE (<https://www.math.ucdavis.edu/~latte/>) for counting lattice points and integration inside convex polytopes and 4ti2 (<http://www.4ti2.de/>) for algebraic, geometric, and combinatorial problems on linear spaces and front-end tools facilitating their use in the R ecosystem.

Assign meaningful labels to data frame columns. labelmachine manages your label assignment rules in yaml files and makes it easy to use the same labels in multiple projects.

Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.

This package provides utilities to detect common data leakage patterns including train/test contamination, temporal leakage, and data duplication, enhancing model reliability and reproducibility in machine learning workflows. Generates diagnostic reports and visual summaries to support data validation. Methods based on best practices from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0387848570).

This package provides bindings to the Leaflet.glify JavaScript library which extends the leaflet JavaScript library to render large data in the browser using WebGl'.

Location and scale hypothesis testing using the LePage test and variants of its as proposed by Hussain A. and Tsagris M. (2025), <doi:10.48550/arXiv.2509.19126>.

Automatically returns results from 18 logistic models including 14 individual logistic models and 4 logistic ensembles of models. The package also returns 25 plots, 5 tables, and a summary report. The package automatically builds all 18 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. The package includes medical data (the Pima Indians data set), and information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver Operating Characteristic (ROC) curves for each of the 18 models.

An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.

Companion toolbox for structural equation models fitted with lavaan'. Provides post-estimation diagnostics and graphics that operate directly on a fitted object using its estimates and covariance, and refits auxiliary models when needed. The package relies on lavaan (Rosseel, 2012) <doi:10.18637/jss.v048.i02>.

This package provides a static library for Imath (see <https://github.com/AcademySoftwareFoundation/Imath>), a library for functions and data types common in computer graphics applications, including a 16-bit floating-point type.