PairViz, scagnostics, rgl, grid, MASS, rggobi, RGtk2, hexbin,
RDRToolbox, vegan, RnavGraphImageData, png, Rgraphviz

PairViz, scagnostics, rgl, MASS, hexbin, RDRToolbox, vegan,
RnavGraphImageData, png, Rgraphviz
'AllGenerics.R' 'ng_data.R' 'ng_graph.R' 'ng_path.R'
'ng_image.R' 'SettingClasses.R' 'NavGraphHandler.R'
'GraphDisplay.R' 'Visualization.R' 'Visualization2D.R'
'Viz2D_Axis.R' 'Viz2D_tk2d.R' 'FunctionNavGraph.R'
'graphtools.R' 'navGraph.R' 'scagNav.R' 'zzz.R'

Using Graphs as a Navigational Infrastructure.
PairViz, MASS, hexbin, RDRToolbox, vegan, RnavGraphImageData,
png, Rgraphviz

Using Graphs as a Navigational Infrastructure

Some image data used in the RnavGraph package demos
Image data used as examples in the RnavGraph R package.
See the demos in the RnavGraph package.

Image data used as examples in the RnavGraph R package. See the demos in the RnavGraph package.

Image Data Used in the Loon Package Demos
c(
person("Adrian", "Waddell", , "adrian@waddell.ch", role = c("aut", "cre")),
person("R. Wayne", "Oldford", , "rwoldford@uwaterloo.ca", role = "aut")
)
Image data used as examples in the loon R package.
Adrian Waddell [aut, cre],
R. Wayne Oldford [aut]

Using as input a 'LaTeX' file with a multiple choice exam, this package will produce random versions of the same exam.

tools, stringr, stringi, data.table, stringdist
knitr, rmarkdown, testthat, ggplot2, googlesheets

Build and Grade Multiple Choice Exams with Randomized Content
Using as input a 'LaTeX' file with a multiple choice exam, this package will produce several versions with randomized contents of the same exam. Functions for grading and testing for cheating are also available.
knitr, rmarkdown, testthat, ggplot2, irtoys

Using as input a 'LaTeX' file with a multiple choice exam, this package will produce several versions with randomized contents of the same exam. Functions for grading are also available.

Resistance Relationship Networks using Graphical LASSO
person("William", "Love", email = "wjlove@ncsu.edu", role = c("aut", "cre"))
Novel methods are needed to analyze the large amounts of antimicrobial resistance (AMR) data generated by AMR surveillance programs. This package is used to estimate resistance relationship networks, or 'Rnets', from empirical antimicrobial susceptibility data. These networks can be used to study relationships between antimicrobial resistances (typically measured using MICs) and genes in populations. The 'GitHub' for this package is available at <https://GitHub.com/EpidemiologyDVM/Rnets>. Bug reports and features requests should be directed to the same 'GitHub' site. The methods used in 'Rnets' are available in the following publications: An overview of the method in WJ Love, et al., "Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012" (2016) <doi:10.1371/journal.pcbi.1005160>; The graphical LASSO for sparsity in J Friedman, T Hastie, R Tibshirani "Sparse inverse covariance estimation with the graphical lasso" (2007) <doi:10.1093/biostatistics/kxm045>; L1 penalty selection in H Liu, K Roeder, L Wasserman "Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models" (2010) <arXiv:1006.3316>; Modularity for graphs with negative edge weights in S Gomez, P Jensen, A Arenas. "Analysis of community structure in networks of correlated data" (2009) <doi:10.1103/PhysRevE.80.016114>.
'Rnet_classes.R' 'Assign_Emetadata.R' 'Assign_Vmetadata.R'
'Clean_Sigma.R' 'Estimate_pCorrs.R' 'GenR.R'
'Gomez_modularity_v6_1.R' 'L1_selection.R' 'Rnet.R'
'Rnet_heatmap.r' 'Sq2L.R' 'data_e_attrs.R' 'data_ec_coords.R'
'data_narms_ec.r' 'data_v_attrs.R' 'methods-plot.R'
'methods-print.R' 'methods-summary.R'