Showing 5 of total 5 results (show query)
cran
network:Classes for Relational Data
Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
Maintained by Carter T. Butts. Last updated 4 months ago.
3 stars 7.65 score 146 dependents
quanteda
quanteda.textplots:Plots for the Quantitative Analysis of Textual Data
Plotting functions for visualising textual data. Extends 'quanteda' and related packages with plot methods designed specifically for text data, textual statistics, and models fit to textual data. Plot types include word clouds, lexical dispersion plots, scaling plots, network visualisations, and word 'keyness' plots.
Maintained by Kenneth Benoit. Last updated 7 months ago.
7 stars 6.77 score 648 scripts
statnet
lolog:Latent Order Logistic Graph Models
Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <arXiv:1804.04583> for a detailed description of the methods.
Maintained by Ian E. Fellows. Last updated 1 years ago.
5 stars 5.56 score 72 scripts
cnrakt
haplotypes:Manipulating DNA Sequences and Estimating Unambiguous Haplotype Network with Statistical Parsimony
Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel coding methods (only simple indel coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapses identical DNA sequences into haplotypes or inferring haplotypes using user provided absolute pairwise character difference matrix. This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks.
Maintained by Caner Aktas. Last updated 2 years ago.
1 stars 3.43 score 54 scriptscran
deal:Learning Bayesian Networks with Mixed Variables
Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
Maintained by Claus Dethlefsen. Last updated 2 years ago.
2.70 score