An implementation of locally Gaussian distributions. It provides methods for implementing locally Gaussian multivariate density estimation, conditional density estimation, various independence tests for iid and time series data, a test for conditional independence and a test for financial contagion.

This package implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models. Copula family and local likelihood bandwidth parameters are selected by leave-one-out cross-validation. The models are implemented in TMB', meaning that the local score function is efficiently calculated via automated differentiation (AD), such that quasi-Newton algorithms may be used for parameter estimation.

This package provides a model-agnostic safety layer for developers building with large language model (LLM) applications. Maps starter controls to the Open Worldwide Application Security Project Top 10 for Large Language Model Applications 2025 risk categories <https://genai.owasp.org/llm-top-10/> via a modular rule engine. Supports regular-expression rules, lightweight natural language processing (NLP) intent checks, optional scanners, and semantic large language model reviewer checks on prompts, conversations, retrieved context, tool inputs and outputs, streaming chunks, and model outputs. Supports workflows with the Ollama local web service <https://ollama.com/> via ellmer', remote reviewer endpoints, and other chat interfaces callable from R'. Intended as an experimental guardrail layer that teams should evaluate against their own workflows before relying on it in production.

This package provides a variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses.

This package implements a fast and resistant divisive clustering algorithm which identifies a specified number of clusters: lumbermark iteratively chops off sizeable limbs that are joined by protruding segments of a dataset's mutual reachability minimum spanning tree; see Gagolewski (2026) <https://lumbermark.gagolewski.com/>. The use of a mutual reachability distance pulls peripheral points farther away from each other. When combined with the deadwood package, it can act as an outlier detector. The Python version of lumbermark is available via PyPI'.

Resolves ambiguous links between data.frames using large language models (LLMs). Supports matching across spelling variations, translations, and differing levels of precision.

This package provides a Shiny graphical interface for the complete workflow of Latent Dirichlet Allocation (LDA) topic modelling on bibliometric data from Scopus and Web of Science. Steps include data import and deduplication, text preprocessing (stopword removal, stemming, n-grams, sparse-term filtering), statistical inference to select the optimal number of topics via coherence, final model training, and topic trend analysis over time using linear regression. All results can be exported as Excel files, RDS objects, and publication-quality plots.

This package provides a set of functions and tools to conduct acoustic source localization, as well as organize and check localization data and results. The localization functions implement the modified steered response power algorithm described by Cobos et al. (2010) <doi:10.1109/LSP.2010.2091502>.

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.

Probabilistic record linkage without direct identifiers using only diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ; Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane & Cai (2021) <doi: 10.1093/jamia/ocab187>.

Estimating causal parameters in the presence of treatment spillover is of great interest in statistics. This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form. The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect. The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.

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.

Data, scripts and code from chunks used as examples in the book "Learn R: As a Language" 1ed and 2ed by Pedro J. Aphalo. ISBN 9780367182533 (pbk 1ed); ISBN 9780367182557 (hbk 1ed); ISBN 9780429060342 (ebk 1ed).

This package provides functions for regional frequency analysis using the methods of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis: an approach based on L-moments".

Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.

Simple functions to lookup items in key-value pairs. See Mehta (2021) <doi:10.1007/978-1-4842-6613-7_6>.

This package provides functions to upload vectorial data and derive landscape connectivity metrics in habitat or matrix systems. Additionally, includes an approach to assess individual patch contribution to the overall landscape connectivity, enabling the prioritization of habitat patches. The computation of landscape connectivity and patch importance are very useful in Landscape Ecology research. The metrics available are: number of components, number of links, size of the largest component, mean size of components, class coincidence probability, landscape coincidence probability, characteristic path length, expected cluster size, area-weighted flux and integral index of connectivity. Pascual-Hortal, L., and Saura, S. (2006) <doi:10.1007/s10980-006-0013-z> Urban, D., and Keitt, T. (2001) <doi:10.2307/2679983> Laita, A., Kotiaho, J., Monkkonen, M. (2011) <doi:10.1007/s10980-011-9620-4>.

This package provides a diverse collection of georeferenced and spatial datasets from different domains including urban studies, housing markets, environmental monitoring, transportation, and socio-economic indicators. The package consolidates datasets from multiple open sources such as Kaggle, chopin, spData, adespatial, and bivariateLeaflet. It is designed for researchers, analysts, and educators interested in spatial analysis, geostatistics, and geographic data visualization. The datasets include point patterns, polygons, socio-economic data frames, and network-like structures, allowing flexible exploration of geospatial phenomena.

The lognormal distribution (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>) can characterize uncertainty that is bounded by zero. This package provides estimation of distribution parameters, computation of moments and other basic statistics, and an approximation of the distribution of the sum of several correlated lognormally distributed variables (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the difference of two correlated lognormally distributed variables (Lo 2012 <doi:10.1155/2012/838397>).

Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the LongCART and SurvCART algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

Robust test(s) for model diagnostics in regression. The current version contains a robust test for functional specification (linearity). The test is based on the robust bounded-influence test by Heritier and Ronchetti (1994) <doi:10.1080/01621459.1994.10476822>.

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 provides simple, reproducible access to datasets from the Los Angeles Open Data portal <https://data.lacity.org/>. Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API.

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.