Package: idopNetwork 0.1.2

idopNetwork: A Network Tool to Dissect Spatial Community Ecology

Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their 'dynamic' form. 'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) <doi:10.1080/19490976.2022.2106103>.

Authors:Ang Dong

idopNetwork_0.1.2.tar.gz
idopNetwork_0.1.2.zip(r-4.7)idopNetwork_0.1.2.zip(r-4.6)idopNetwork_0.1.2.zip(r-4.5)
idopNetwork_0.1.2.tgz(r-4.6-any)idopNetwork_0.1.2.tgz(r-4.5-any)
idopNetwork_0.1.2.tar.gz(r-4.7-any)idopNetwork_0.1.2.tar.gz(r-4.6-any)
idopNetwork_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
idopNetwork/json (API)

# Install 'idopNetwork' in R:
install.packages('idopNetwork', repos = c('https://cxzdsa2332.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cxzdsa2332/idopnetwork/issues

Datasets:

On CRAN:

Conda:

4.60 score 8 stars 3 scripts 233 downloads 40 exports 39 dependencies

Last updated from:a1a00a2789. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE162
source / vignettesOK217
linux-release-x86_64NOTE158
macos-release-arm64NOTE174
macos-oldrel-arm64NOTE178
windows-develNOTE165
windows-releaseNOTE102
windows-oldrelNOTE114
wasm-releaseOK124

Exports:bifun_clubifun_clu_convertbifun_clu_parallelbifun_clu_plotbiget_par_intbipower_equation_plotbiqdODE_plot_allbiqdODE_plot_basedarkendata_cleaningdata_matchfun_clufun_clu_BICfun_clu_convertfun_clu_parallelfun_clu_plotfun_clu_selectget_biSAD1get_interactionget_legendre_matrixget_legendre_parget_muget_mu2get_par_intget_SAD1_covmatrixlegendre_fitlogsumexpnetwork_conversionnetwork_maxeffectnetwork_plotnormalizationpower_equationpower_equation_allpower_equation_basepower_equation_fitpower_equation_plotqdODE_allqdODE_parallelqdODE_plot_allqdODE_plot_base

Dependencies:clicodetoolscpp11deSolvefarverforeachggplot2glmnetgluegtableigraphisobanditeratorslabelinglatticelifecyclemagrittrMatrixmvtnormorthopolynompatchworkpkgconfigplyrpolynomR6RColorBrewerRcppRcppEigenreshape2rlangS7scalesshapestringistringrsurvivalvctrsviridisLitewithr

idopNetwork_vignette

Ang Dong

Rendered fromidopNetwork_vignette.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2022-11-23
Started: 2022-11-23

Readme and manuals

Help Manual

Help pageTopics
main function for bifunctional clusteringbifun_clu
convert result of bifunctional clustering resultbifun_clu_convert
parallel version for functional clusteringbifun_clu_parallel
bifunctional clustering plotbifun_clu_plot
acquire initial parameters for functional clusteringbiget_par_int
plot power equation fitting results for bi-variate modelbipower_equation_plot
Q-function to replace log-likelihood functionbiQ_function
plot all decompose plot for two databiqdODE_plot_all
plot single decompose plot for two databiqdODE_plot_base
make color more darkdarken
remove observation with too many 0 valuesdata_cleaning
match power_equation fit result for bi-variate modeldata_match
main function for functional clusteringfun_clu
plot BIC results for functional clusteringfun_clu_BIC
convert result of functional clustering resultfun_clu_convert
parallel version for functional clusteringfun_clu_parallel
functional clustering plotfun_clu_plot
select result of functional clustering resultfun_clu_select
generate biSAD1 covariance matrixget_biSAD1
Lasso-based variable selectionget_interaction
generate legendre matrixget_legendre_matrix
use legendre polynomials to fit a given dataget_legendre_par
curve fit with modified logistic functionget_mu
generate mean vectors with ck and stress conditionget_mu2
acquire initial parameters for functional clusteringget_par_int
generate standard SAD1 covariance matrixget_SAD1_covmatrix
gut microbe OTU data (species level)gut_microbe
generate curve based on legendre polynomialslegendre_fit
calculate log-sum-exp valueslogsumexp
mustard microbe OTU datamustard_microbe
convert ODE results(ODE_solving2) to basic network plot tablenetwork_conversion
convert ODE results(ODE_solving2) to basic network plot tablenetwork_maxeffect
generate network plotnetwork_plot
min-max normalizationnormalization
use power equation parameters to generate y valuespower_equation
use power equation to fit observed valuespower_equation_all
use power equation to fit observed valuespower_equation_base
use power equation to fit given datasetpower_equation_fit
plot power equation fitting resultspower_equation_plot
Q-function to replace log-likelihood functionQ_function
wrapper for qdODE modelqdODE_all
legendre polynomials fit to qdODE modelqdODE_fit
least-square fit for qdODE modelqdODE_ls
wrapper for qdODE_all in parallel versionqdODE_parallel
plot all decompose plotqdODE_plot_all
plot single decompose plotqdODE_plot_base
quasi-dynamic lotka volterra modelqdODEmod
convert qdODE results to plot dataqdODEplot_convert