Package: lpNet 2.44.0
Lars Kaderali
lpNet: Linear Programming Model for Network Inference
lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used.
Authors:
lpNet_2.44.0.tar.gz
lpNet_2.44.0.zip(r-4.7)lpNet_2.44.0.zip(r-4.6)lpNet_2.44.0.zip(r-4.5)
lpNet_2.44.0.tgz(r-4.6-any)lpNet_2.44.0.tgz(r-4.5-any)
lpNet_2.44.0.tar.gz(r-4.7-any)lpNet_2.44.0.tar.gz(r-4.6-any)
lpNet_2.44.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
lpNet/json (API)
NEWS
| # Install 'lpNet' in R: |
| install.packages('lpNet', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:lpNet-2.45.0(bioc 3.24)lpNet-2.44.0(bioc 3.23)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:2f712817aa (on RELEASE_3_23). Checks:1 ERROR, 7 WARNING, 2 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | ERROR | 145 | ||
| linux-devel-x86_64 | WARNING | 139 | ||
| source / vignettes | OK | 202 | ||
| linux-release-x86_64 | WARNING | 150 | ||
| macos-release-arm64 | WARNING | 77 | ||
| macos-oldrel-arm64 | WARNING | 73 | ||
| windows-devel | WARNING | 90 | ||
| windows-release | WARNING | 80 | ||
| windows-oldrel | WARNING | 88 | ||
| wasm-release | OK | 95 |
Exports:calcActivationcalcPredictionKfoldCVcalcPredictionLOOCVcalcRangeLambdadoILPgenerateTimeSeriesNetStatesgetAdjagetBaselinegetEdgeAnnotgetObsMatgetSampleAdjagetSampleAdjaMADkfoldCVloocvsummarizeRepl
Dependencies:BiocGenericsbitopsgenericsgraphKEGGgraphlpSolveRCurlRgraphvizXML
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Network Inference Of Perturbation Data Using a Linear Programming Approach. | lpNet-package lpNet |
| Calculate Activation Matrix | calcActivation |
| Calculate Predicted Observation. | calcPredictionKfoldCV calcPredictionLOOCV |
| Compute Range Of Penalty Parameter Lambda. | calcRangeLambda |
| Cross-validation | kfoldCV loocv |
| Do The Network Inference With The Linear Programming Approach. | doILP |
| Generate Time Series Network States | generateTimeSeriesNetStates |
| Get Adjacency Matrix. | getAdja |
| Get Baseline Vector. | getBaseline |
| Get the annotation of the edges. | getEdgeAnnot |
| Get Observation Matrix. | getObsMat |
| Get The Sample Adjacency. | getSampleAdja getSampleAdjaMAD |
| Summarize Replicate Measurements | summarizeRepl |
