This package provides tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.

This package provides a fast generalized edit distance and string alignment computation mainly for linguistic aims. As a generalization to the classic edit distance algorithms, the package allows users to define custom cost for every symbol's insertion, deletion, and substitution. The package also allows character combinations in any length to be seen as a single symbol which is very useful for International Phonetic Alphabet (IPA) transcriptions with diacritics. In addition to edit distance result, users can get detailed alignment information such as all possible alignment scenarios between two strings which is useful for testing, illustration or any further usage. Either the distance matrix or its long table form can be obtained and tools to do such conversions are provided. All functions in the package are implemented in C++ and the distance matrix computation is parallelized leveraging the RcppThread package.

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>.

This package implements code to identify lexical competitors in a given list of words. We include many of the standard competitor types used in spoken word recognition research, such as functions to find cohorts, neighbors, and rhymes, amongst many others. The package includes documentation for using a variety of lexicon files, including those with form codes made up of multiple letters (i.e., phoneme codes) and also basic orthographies. Importantly, the code makes use of multiple CPU cores and vectorization when possible, making it extremely fast and able to handle large lexicons. Additionally, the package contains documentation for users to easily write new functions, allowing researchers to examine other relationships within a lexicon. Preprint: <https://osf.io/preprints/psyarxiv/8dyru/>. Open access: <doi:10.3758/s13428-021-01667-6>. Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). <doi:10.3758/s13428-021-01667-6>.

Download and read data on lobbying in the United States Congress. Data is queried from the Senate's Application Programming Interface (<https://lda.senate.gov/api/>). This supports filings since 2008. Functions exist for all primary data endpoints, including queries by filings, contributions, registrations, clients, and lobbyists.

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.

This package provides a statistical learning method that tries to find the best set of predictors and interactions between predictors for modeling binary or quantitative response data in a decision tree. Several search algorithms and ensembling techniques are implemented allowing for finetuning the method to the specific problem. Interactions with quantitative covariables can be properly taken into account by fitting local regression models. Moreover, a variable importance measure for assessing marginal and interaction effects is provided. Implements the procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Create custom labels, badges, certificates and other documents. Automate the production of potentially large numbers of herbarium and collection labels, accreditation badges, attendance and participation certificates, etc, and deliver them automatically. Documents are generated in PDF format, which requires a working installation of LaTeX', such as TinyTeX'.

Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.

The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <doi:10.48550/arXiv.1809.06255>. For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>. For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.

Utilities for querying plain text accounting files from Ledger', HLedger', and Beancount'.

Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) <doi:10.1111/geer.12146>.

Simplifies the whole process of creating stacked tilted maps, that are often used in scientific publications to show different environmental layers for a geographical region. Tilting maps and layering them allows to easily draw visual correlations between these environmental layers.

Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details.

Adds smoothing spline modelling capability to nlme. Fits smoothing spline terms in Gaussian linear and nonlinear mixed-effects models.

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>.

This computes Lipinski Rule of Five parameters and offers visualization for drug discovery. It analyzes molecular properties like molecular weight, hydrogen bond donors, acceptors, and ALogP, providing histograms and pass/fail status plots for efficient compound evaluation, aiding in drug development.

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 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.

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.

Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes. The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.

This package provides functions to fits simple linear regression models with log normal errors and identity link, i.e. taking the responses on the original scale. See Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.

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.