This package produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.<doi:10.1002/sim.9212>.

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

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'.

This package produces a PDF diff of two rmarkdown', quarto', Sweave or TeX files, using the external latexdiff utility.

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).

Exact significance tests for a changepoint in linear or multiple linear regression. Confidence regions with exact coverage probabilities for the changepoint. Based on Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.

Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>. More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

This package provides a method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. See Li et al. (2023) "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". arXiv preprint <arXiv:2208.13074>.

This package provides a nonparametric method to approximate Laplacian graph spectra of a network with ordered vertices. This provides a computationally efficient algorithm for obtaining an accurate and smooth estimate of the graph Laplacian basis. The approximation results can then be used for tasks like change point detection, k-sample testing, and so on. The primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).

Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.

Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) <doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed in Xu He (2018) <doi:10.1093/biomet/asy069>, interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) <doi:10.1137/23M156940X>, (sliced) rotated sphere packing designs proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289> and Xu He (2019) <doi:10.1080/00401706.2018.1458655>, densest packing-based maximum projections designs proposed in Xu He (2020) <doi:10.1093/biomet/asaa057> and Xu He (2018) <doi:10.48550/arXiv.1709.02062>, maximin distance designs for mixed continuous, ordinal, and binary variables proposed in Hui Lan and Xu He (2025) <doi:10.48550/arXiv.2507.23405>, and optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments proposed in Xu He, Junpeng Gong, and Zhaohui Li (2025) <doi:10.48550/arXiv.2506.04582>.

Interact with LaminDB'. LaminDB is an open-source data framework for biology. This package allows you to query and download data from LaminDB instances.

Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>, O'Connor (1999) <doi:10.3758/BF03200753>, Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and Wampold (1995, ISBN:0-89391-919-5).

This package provides extensions to the leaflet package to customize legends with images, text styling, orientation, sizing, and symbology and functions to create symbols to plot on maps.

This package provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.

An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0.

This package provides Shiny widgets and theme that support a Library Computer Access/Retrieval System (LCARS) aesthetic for Shiny apps. The package also includes functions for adding a minimal LCARS theme to static ggplot2 graphs. More details about LCARS can be found at <https://en.wikipedia.org/wiki/LCARS>.

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.

This package provides a utility to facilitate the logging and review of R programs in clinical trial programming workflows.

Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal, multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.

Create tables from within R directly on Google Slides presentations. Currently supports matrix, data.frame and flextable objects.

Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2025a) <doi:10.1080/01621459.2025.2520467>, Kang and Oh (2025b) <doi:10.1080/00031305.2025.2563730>, and Kang (2025) <doi:10.23170/snu.000000188358.11032.0001856>.

Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. The original package description was published as Goslee (2011) <doi:10.18637/jss.v043.i04>, and details of the topographic corrections in Goslee (2012) <doi:10.14358/PERS.78.9.973>.

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