Network Data Operations and Overlapping Partitions Based Methods for Large Networks

Implements methods for generating, embedding, and clustering random networks and for estimating and selecting statistical network models. Provides SONNET (Subsampling ON NETwork), a scalable subsampling-based divide-and-conquer method for community detection described by Chakrabarty, Sengupta and Chen (2025) , and NETCROP (NETwork CRoss-validation using Overlapping Partitions), an overlapping-partition framework for network cross-validation, model selection, and regularization tuning described by Chakrabarty, Sengupta and Chen (2026) . Also includes spectral and latent-space methods, loss functions, and helper functions for statistical analysis of network data.

A Shiny Application for End-to-End Bayesian Decision Network Analysis and Web-Deployment

A Shiny application for learning Bayesian Decision Networks from data. This package can be used for probabilistic reasoning (in the observational setting), causal inference (in the presence of interventions) and learning policy decisions (in Decision Network setting). Functionalities include end-to-end implementations for data-preprocessing, structure-learning, exact inference, approximate inference, extending the learned structure to Decision Networks and policy optimization using statistically rigorous methods such as bootstraps, resampling, ensemble-averaging and cross-validation. In addition to Bayesian Decision Networks, it also features correlation networks, community-detection, graph visualizations, graph exports and web-deployment of the learned models as Shiny dashboards.

Client for the 'AstraeaDB' Graph Database

Provides a client for 'AstraeaDB', a graph database with vector search capabilities. Supports node and edge create, read, update, and delete operations, label and edge-type lookups, graph traversals (breadth-first search, depth-first search, shortest path), temporal (time-travel) queries, graph algorithms (PageRank, Louvain community detection, connected components, and degree and betweenness centrality), vector similarity search, hybrid graph-vector search, Graph Query Language (GQL) execution, and graph-based retrieval-augmented generation (subgraph extraction with large language model integration). Communicates with the 'AstraeaDB' server over a JSON-over-TCP protocol. An optional 'Apache Arrow Flight' transport is available for high-performance bulk operations when the 'arrow' package is installed.

Sniffing Emergence and Trajectories in Academic Papers and Patents

Provides a unified set of methods to detect scientific emergence and technological trajectories in academic papers and patents. The package combines citation network analysis with community detection and attribute extraction, also applying natural language processing (NLP) and structural topic modeling (STM) to uncover the contents of research communities. It implements metrics and visualizations of community trajectories, including novelty indicators, citation cycle time, and main path analysis, allowing researchers to map and interpret the dynamics of emerging knowledge fields. Applications of the method include: Souza et al. (2022) , Souza et al. (2022) , Matos et al. (2023) , Maria et al. (2023) , Biazatti et al. (2024) , Felizardo et al. (2025) , and Miranda et al. (2025) .

Quantifying Animal Movement and Space-Use Patterns with Statistical Physics

Provides tools to analyse animal movement and space-use patterns from telemetry data using methods derived from statistical physics. Methods span displacement-based approaches, distribution fitting, space-use metrics (including the influence of correlations on space-use), network-based community detection, and measures of entropy and predictability. The package enables characterisation of these patterns across spatial and temporal scales, including variation within and among individuals (inter- and intraspecific analyses). Outputs include interpretable metrics and visualisations to support ecological analysis and the investigation of fundamental movement processes. For applications of these methods in ecological studies see Rodríguez et al. (2017) and Sequeira et al. (2018) .

Lightweight 'CUDA' Numerical Computing

Provides a lightweight interface to graphics processing unit (GPU)-accelerated numerical computing using 'CUDA'. Dense tensors, sparse matrices, decompositions, distances, exact nearest neighbours, clustering, graph workflows, and embeddings share one consistent interface. The native backend discovers the 'NVIDIA CUDA Driver API', 'cuBLAS', and 'cuSOLVER' libraries at runtime without bundling 'LibTorch' or the 'CUDA Runtime'. Stage-level provenance records the backend, device, and data transfers used by each result. A portable implementation supports package validation on systems without 'CUDA'. Background for the included Leiden community detection and uniform manifold approximation and projection methods is given by Traag, Waltman and van Eck (2019) and McInnes et al. (2018) , respectively.

Functions for Clustering and Testing of Presence-Absence, Abundance and Multilocus Genetic Data

Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview.

R Database Interface

A database interface definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.

Community Assembly by Traits: Individuals and Beyond

Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models. Provides tools to analyse 'intraspecific' trait variability and its consequences for community assembly. Implements a framework using individual-level trait data to decompose variance at the population, species, and community levels. Methods are described in 'Taudiere' and 'Violle' (2016) .

Change-Points Detections for Changes in Variance

Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, .