The leaflet JavaScript library provides many plugins some of which are available in the core leaflet package, but there are many more. It is not possible to support them all in the core leaflet package. This package serves as an add-on to the leaflet package by providing extra functionality via leaflet plugins.

This package provides Shiny gadgets to search, type, and insert IPA symbols into documents or scripts, requiring only knowledge about phonetics or X-SAMPA'. Also provides functions to facilitate the rendering of IPA symbols in LaTeX and PDF format, making IPA symbols properly rendered in all output formats. A minimal R Markdown template for authoring Linguistics related documents is also bundled with the package. Some helper functions to facilitate authoring with R Markdown is also provided.

Programmatically create R Markdown documents from lists.

Fits structural equation modeling via penalized likelihood.

This package provides a set of functions to locate some programs available on the user machine. The package provides functions to locate Node.js', npm', LibreOffice', Microsoft Word', Microsoft PowerPoint', Microsoft Excel', Python', pip', Mozilla Firefox and Google Chrome'. User can test the availability of a program with eventually a version and call it with function system2() or system(). This allows the use of a single function to retrieve the path to a program regardless of the operating system and its configuration.

Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.

Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.

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>.

Approximate marginal maximum likelihood estimation of multidimensional latent variable models via adaptive quadrature or Laplace approximations to the integrals in the likelihood function, as presented for confirmatory factor analysis models in Jin, S., Noh, M., and Lee, Y. (2018) <doi:10.1080/10705511.2017.1403287>, for item response theory models in Andersson, B., and Xin, T. (2021) <doi:10.3102/1076998620945199>, and for generalized linear latent variable models in Andersson, B., Jin, S., and Zhang, M. (2023) <doi:10.1016/j.csda.2023.107710>. Models implemented include the generalized partial credit model, the graded response model, and generalized linear latent variable models for Poisson, negative-binomial and normal distributions. Supports a combination of binary, ordinal, count and continuous observed variables and multiple group models.

This package provides access to the Leanpub API <https://leanpub.com/help/api> for gathering information about publications and submissions to the Leanpub platform.

This package provides a collection of tools for interactive manipulation of (spatial) data layers on leaflet web maps. Tools include editing of existing layers, creation of new layers through drawing of shapes (points, lines, polygons), deletion of shapes as well as cutting holes into existing shapes. Provides control over options to e.g. prevent self-intersection of polygons and lines or to enable/disable snapping to align shapes.

This package provides R with the Glottolog database <https://glottolog.org/> and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <https://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages <https://docs.ropensci.org/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.

Lazy read for drawings. A dplyr back end for data sources supported by GDAL vector drivers, that allows working with local or remote sources as if they are in-memory data frames. Basic features works with any drawing format ('GDAL vector data source') supported by the sf package.

This package contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.

This package performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages eva <https://cran.r-project.org/package=eva>, evd <https://cran.r-project.org/package=evd>, evir <https://cran.r-project.org/package=evir>, extRemes <https://cran.r-project.org/package=extRemes>, fExtremes <https://cran.r-project.org/package=fExtremes>, ismev <https://cran.r-project.org/package=ismev>, mev <https://cran.r-project.org/package=mev>, POT <https://cran.r-project.org/package=POT> and texmex <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the chandwich package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the sandwich package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.

Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).

This package provides modular, graph-based agents powered by large language models (LLMs) for intelligent task execution in R. Supports structured workflows for tasks such as forecasting, data visualization, feature engineering, data wrangling, data cleaning, SQL', code generation, weather reporting, and research-driven question answering. Each agent performs iterative reasoning: recommending steps, generating R code, executing, debugging, and explaining results. Includes built-in support for packages such as tidymodels', modeltime', plotly', ggplot2', and prophet'. Designed for analysts, developers, and teams building intelligent, reproducible AI workflows in R. Compatible with LLM providers such as OpenAI', Anthropic', Groq', and Ollama'. Inspired by the Python package langagent'.

Dataset and functions to explore quality of literary novels. The package is a part of the Riddle of Literary Quality project, and it contains the data of a reader survey about fiction in Dutch, a description of the novels the readers rated, and the results of stylistic measurements of the novels. The package also contains functions to combine, analyze, and visualize these data. For more details, see: Eder M, van Zundert J, Lensink S, van Dalen-Oskam K (2022). Replicating The Riddle of Literary Quality: The litRiddle package for R. In _Digital Humanities 2022: Conference Abstracts_, 636-637.

Linear splines with convenient parametrisations such that (1) coefficients are slopes of consecutive segments or (2) coefficients are slope changes at consecutive knots. Knots can be set manually or at break points of equal-frequency or equal-width intervals covering the range of x'. The implementation follows Greene (2003), chapter 7.2.5.

Network analysis usually requires estimating the uncertainty of graph statistics. Through this package, we provide tools to bootstrap various networks via local bootstrap procedure. Additionally, it includes functions for generating probability matrices, creating network adjacency matrices from probability matrices, and plotting network structures. The reference will be updated soon.

This package provides tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).

This package provides a simple progress bar showing estimated remaining time. Multiple forecast methods and user defined forecast method for the remaining time are supported.

This package provides a flexible and easy-to use interface for the soil vegetation atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran. The model simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation, as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) <doi:10.1175/1525-7541(2003)004%3C1276:SOAETS%3E2.0.CO;2>. A set of high-level functions for model set up, execution and parallelization provides easy access to plot-level SVAT simulations, as well as multi-run and large-scale applications.

This package provides tools to teach students elemental statistics. The main topics covered are descriptive statistics, probability models (discrete and continuous variables) and statistical inference (confidence intervals and hypothesis tests). One of the main advantages of this package is that allows the user to read quite a variety of types of data files with one unique command. Moreover it includes shortcuts to simple but up-to-now not in R descriptive features such a complete frequency table or an histogram with the optimal number of intervals. Related to model distributions (both discrete and continuous), the package allows the student to easy plot the mass/density function, distribution function and quantile function just detailing as input arguments the known population parameters. The inference related tools are basically confidence interval and hypothesis testing. Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared).