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✨
DESCRIPTION
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:

gut_microbe - Gut microbe OTU data
mustard_microbe - Mustard microbe OTU data

On CRAN:

4.70 score 10 stars 4 scripts 276 downloads 40 exports 39 dependencies

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

Target	Result	Time
linux-devel-x86_64	NOTE	157
source / vignettes	OK	218
linux-release-x86_64	NOTE	127
macos-release-arm64	NOTE	170
macos-oldrel-arm64	NOTE	247
windows-devel	NOTE	113
windows-release	NOTE	114
windows-oldrel	NOTE	112
wasm-release	OK	122

Exports:bifun_clu bifun_clu_convert bifun_clu_parallel bifun_clu_plot biget_par_int bipower_equation_plot biqdODE_plot_all biqdODE_plot_base darken data_cleaning data_match fun_clu fun_clu_BIC fun_clu_convert fun_clu_parallel fun_clu_plot fun_clu_select get_biSAD1 get_interaction get_legendre_matrix get_legendre_par get_mu get_mu2 get_par_int get_SAD1_covmatrix legendre_fit logsumexp network_conversion network_maxeffect network_plot normalization power_equation power_equation_all power_equation_base power_equation_fit power_equation_plot qdODE_all qdODE_parallel qdODE_plot_all qdODE_plot_base

Dependencies:cli codetools cpp11 deSolve farver foreach ggplot2 glmnet glue gtable igraph isoband iterators labeling lattice lifecycle magrittr Matrix mvtnorm orthopolynom patchwork pkgconfig plyr polynom R6 RColorBrewer Rcpp RcppEigen reshape2 rlang S7 scales shape stringi stringr survival vctrs viridisLite withr

idopNetwork_vignette

Rendered fromidopNetwork_vignette.Rmdusingknitr::rmarkdown

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

Help page	Topics
main function for bifunctional clustering	bifun_clu
convert result of bifunctional clustering result	bifun_clu_convert
parallel version for functional clustering	bifun_clu_parallel
bifunctional clustering plot	bifun_clu_plot
acquire initial parameters for functional clustering	biget_par_int
plot power equation fitting results for bi-variate model	bipower_equation_plot
Q-function to replace log-likelihood function	biQ_function
plot all decompose plot for two data	biqdODE_plot_all
plot single decompose plot for two data	biqdODE_plot_base
make color more dark	darken
remove observation with too many 0 values	data_cleaning
match power_equation fit result for bi-variate model	data_match
main function for functional clustering	fun_clu
plot BIC results for functional clustering	fun_clu_BIC
convert result of functional clustering result	fun_clu_convert
parallel version for functional clustering	fun_clu_parallel
functional clustering plot	fun_clu_plot
select result of functional clustering result	fun_clu_select
generate biSAD1 covariance matrix	get_biSAD1
Lasso-based variable selection	get_interaction
generate legendre matrix	get_legendre_matrix
use legendre polynomials to fit a given data	get_legendre_par
curve fit with modified logistic function	get_mu
generate mean vectors with ck and stress condition	get_mu2
acquire initial parameters for functional clustering	get_par_int
generate standard SAD1 covariance matrix	get_SAD1_covmatrix
gut microbe OTU data (species level)	gut_microbe
generate curve based on legendre polynomials	legendre_fit
calculate log-sum-exp values	logsumexp
mustard microbe OTU data	mustard_microbe
convert ODE results(ODE_solving2) to basic network plot table	network_conversion
convert ODE results(ODE_solving2) to basic network plot table	network_maxeffect
generate network plot	network_plot
min-max normalization	normalization
use power equation parameters to generate y values	power_equation
use power equation to fit observed values	power_equation_all
use power equation to fit observed values	power_equation_base
use power equation to fit given dataset	power_equation_fit
plot power equation fitting results	power_equation_plot
Q-function to replace log-likelihood function	Q_function
wrapper for qdODE model	qdODE_all
legendre polynomials fit to qdODE model	qdODE_fit
least-square fit for qdODE model	qdODE_ls
wrapper for qdODE_all in parallel version	qdODE_parallel
plot all decompose plot	qdODE_plot_all
plot single decompose plot	qdODE_plot_base
quasi-dynamic lotka volterra model	qdODEmod
convert qdODE results to plot data	qdODEplot_convert

Package: idopNetwork 0.1.2

idopNetwork: A Network Tool to Dissect Spatial Community Ecology

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Help Manual

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