Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 6239 packages in 0.02 seconds

alcyon — by Petros Koutsolampros, a year ago

Spatial Network Analysis

Interface package for 'sala', the spatial network analysis library from the 'depthmapX' software application. The R parts of the code are based on the 'rdepthmap' package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) < https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's handbook".

networkR — by Claus Thorn Ekstrøm, 6 months ago

Network Analysis and Visualization

Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135).

rENA — by Cody L Marquart, a month ago

Epistemic Network Analysis

ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).

multinets — by Neylson Crepalde, 6 years ago

Multilevel Networks Analysis

Analyze multilevel networks as described in Lazega et al (2008) and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to 'igraph'.

Diderot — by Christian Vincenot, 6 years ago

Bibliographic Network Analysis

Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.

dyads — by Bonne J.H. Zijlstra, 4 years ago

Dyadic Network Analysis

Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, ) and p2 (Van Duijn, Snijders & Zijlstra, 2004, ), the multilevel p2 model (Zijlstra, Van Duijn & Snijders (2009) ), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) , the (multilevel) b2 model.

NAIR — by Brian Neal, 2 years ago

Network Analysis of Immune Repertoire

Pipelines for studying the adaptive immune repertoire of T cells and B cells via network analysis based on receptor sequence similarity. Relate clinical outcomes to immune repertoires based on their network properties, or to particular clusters and clones within a repertoire. Yang et al. (2023) .

MetaNet — by Chen Peng, 2 months ago

Network Analysis for Omics Data

Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.

nett — by Arash A. Amini, 3 years ago

Network Analysis and Community Detection

Features tools for the network data analysis and community detection. Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models. Implements the following: Amini, Chen, Bickel and Levina (2013) Bickel and Sarkar (2015) Lei (2016) Wang and Bickel (2017) Zhang and Amini (2020) Le and Levina (2022) .

tna — by Sonsoles López-Pernas, a month ago

Transition Network Analysis (TNA)

Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) for more details on TNA.