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

Found 952 packages in 0.01 seconds

networkGen — by Bao Sheng Loe (Aiden), 6 years ago

Network Maze Generator

A network Maze generator that creates different types of network mazes.

PLEXI — by Behnam Yousefi, 8 months ago

Multiplex Network Analysis

Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) .

econetwork — by Vincent Miele, a year ago

Analyzing Ecological Networks

A collection of advanced tools, methods and models specifically designed for analyzing different types of ecological networks - especially antagonistic (food webs, host-parasite), mutualistic (plant-pollinator, plant-fungus, etc) and competitive networks, as well as their variability in time and space. Statistical models are developed to describe and understand the mechanisms that determine species interactions, and to decipher the organization of these ecological networks (Ohlmann et al. (2019) , Gonzalez et al. (2020) , Miele et al. (2021) , Botella et al (2021) ).

netregR — by Frank W. Marrs, 6 years ago

Regression of Network Responses

Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) .

modnets — by Trevor Swanson, 2 years ago

Modeling Moderated Networks

Methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) < https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.

CliquePercolation — by Jens Lange, a year ago

Clique Percolation for Networks

Clique percolation community detection for weighted and unweighted networks as well as threshold and plotting functions. For more information see Farkas et al. (2007) and Palla et al. (2005) .

ckanr — by Francisco Alves, a year ago

Client for the Comprehensive Knowledge Archive Network ('CKAN') API

Client for 'CKAN' API (< https://ckan.org/>). Includes interface to 'CKAN' 'APIs' for search, list, show for packages, organizations, and resources. In addition, provides an interface to the 'datastore' API.

ppsbm — by Daphné Giorgi, 6 years ago

Clustering in Longitudinal Networks

Stochastic block model used for dynamic graphs represented by Poisson processes. To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion. Y. Baraud and L. Birgé (2009). . C. Biernacki, G. Celeux and G. Govaert (2000). . M. Corneli, P. Latouche and F. Rossi (2016). . J.-J. Daudin, F. Picard and S. Robin (2008). . A. P. Dempster, N. M. Laird and D. B. Rubin (1977). < http://www.jstor.org/stable/2984875>. G. Grégoire (1993). < http://www.jstor.org/stable/4616289>. L. Hubert and P. Arabie (1985). . M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). . C. Matias, T. Rebafka and F. Villers (2018). . C. Matias and S. Robin (2014). . H. Ramlau-Hansen (1983). . P. Reynaud-Bouret (2006). .

randnet — by Tianxi Li, 10 months ago

Random Network Model Estimation, Selection and Parameter Tuning

Model selection and parameter tuning procedures for a class of random network models. The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) , likelihood ratio method from Wang and Bickel (2015) , spectral methods from Le and Levina (2015) . Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 ) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 ). It also includes the consensus clustering of Gao et. al. (2014) , the method of moments estimation of nomination SBM of Li et. al. (2020) , and the network mixing method of Li and Le (2021) . It also includes the informative core-periphery data processing method of Miao and Li (2021) . The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Opportunistic — by Christian E. Galarza, 7 years ago

Routing Distribution, Broadcasts, Transmissions and Receptions in an Opportunistic Network

Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.