Computes Statistics for Relational Event History Data

Computes a variety of statistics for relational event models (Meijerink et al., 2022, ). Relational event models enable researchers to investigate exogenous and endogenous factors, and interactions, influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, ), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, ), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models.

Approximate Bayesian Regularization for Parsimonious Estimates

Approximate Bayesian regularization using Gaussian approximations. The input is a vector of estimates and a Gaussian error covariance matrix of the key parameters. Bayesian shrinkage is then applied to obtain parsimonious solutions. The method is described on Karimova, van Erp, Leenders, and Mulder (2025) . Gibbs samplers are used for model fitting. The shrinkage priors that are supported are Gaussian (ridge) priors, Laplace (lasso) priors (Park and Casella, 2008 ), and horseshoe priors (Carvalho, et al., 2010; ). These priors include an option for grouped regularization of different subsets of parameters (Meier et al., 2008; ). F priors are used for the penalty parameters lambda^2 (Mulder and Pericchi, 2018 ). This correspond to half-Cauchy priors on lambda (Carvalho, Polson, Scott, 2010 ).

Simple Key-Value Database using SQLite

Simple key-value database using SQLite as the backend.

Multivariate Time Series Plot

A function for plotting multivariate time series data.

Ordination Methods for the Analysis of Beta-Diversity Indices

The analysis of different aspects of biodiversity requires specific algorithms. For example, in regionalisation analyses, the high frequency of ties and zero values in dissimilarity matrices produced by Beta-diversity turnover produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, visualisation of biogeographical regionalisation can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to visualise and analyse patterns of biodiversity and to improve occurrence data for cryptic taxa.

A Collection of Empirical and Simulated Relational Event Data Sequences

Empirical and simulated data for relational event analyses. Each dataset consists of a relational event sequence and optional actor attributes. Individual datasets are redistributed under their original licenses as documented in inst/DATA_LICENSES.

Import and Export Data

Import and export data from the most common statistical formats by using R functions that guarantee the least loss of the data information, giving special attention to the date variables and the labelled ones.

Optimization Frameworks for Tie-Oriented and Actor-Oriented Relational Event Models

Tools for fitting, diagnosing, and analyzing tie-oriented and actor-oriented relational event models, under both frequentist and Bayesian approaches. The package supports tie-oriented modeling (Butts, 2008, ) and an actor-oriented modeling framework (Stadtfeld et al., 2017, ), with additional model diagnostics and goodness-of-fit tools. Interfaces to estimation backends provide a range of extensions: random-effects (frailty) relational event models capturing sender, receiver, and dyadic heterogeneity (Juozaitiene & Wit 2024, ; Mulder & Hoff, 2024, ), finite mixture and dyadic latent class models for unobserved dyadic heterogeneity (Lakdawala et al., 2026, ), penalized estimation via the lasso, ridge, and elastic net (Tibshirani, R., 1996, ; Karimova et al., 2023, ), and approximate Bayesian regularization (Karimova et al., 2025, ). Modeling of events with a duration is also supported (Lakdawala et al., 2026, ) and moving window relational event models (Mulder & Leenders, 2019, ; Meijerink et al., 2023, ).

Performance Loss Rate Analysis Pipeline

The pipeline contained in this package provides tools used in the Solar Durability and Lifetime Extension Center (SDLE) for the analysis of Performance Loss Rates (PLR) in real world photovoltaic systems. Functions included allow for data cleaning, feature correction, power predictive modeling, PLR determination, and uncertainty bootstrapping through various methods . The vignette "Pipeline Walkthrough" gives an explicit run through of typical package usage. This material is based upon work supported by the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE-0008172. This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.

Geostatistics Methods and Klovan Data

A comprehensive set of geostatistical, visual, and analytical methods, in conjunction with the expanded version of the acclaimed J.E. Klovan's mining dataset, are included in 'klovan'. This makes the package an excellent learning resource for Principal Component Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical techniques. Originally published in the 1976 book 'Geological Factor Analysis', the included mining dataset was assembled by Professor J. E. Klovan of the University of Calgary. Being one of the first applications of FA in the geosciences, this dataset has significant historical importance. As a well-regarded and published dataset, it is an excellent resource for demonstrating the capabilities of PCA, FA, kriging, and other geostatistical techniques in geosciences. For those interested in these methods, the 'klovan' datasets provide a valuable and illustrative resource. Note that some methods require the 'RGeostats' package. Please refer to the README or Additional_repositories for installation instructions. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104.