Make example data with four groups | Fit the model | Posterior distribtions | Model coefficients and intercept | Make predictions
Package: graper 1.29.0
Britta Velten
graper: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes
This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach.
Authors:
graper_1.29.0.tar.gz
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graper_1.29.0.tgz(r-4.6-x86_64)graper_1.29.0.tgz(r-4.6-arm64)graper_1.29.0.tgz(r-4.5-x86_64)graper_1.29.0.tgz(r-4.5-arm64)
graper_1.29.0.tar.gz(r-4.7-arm64)graper_1.29.0.tar.gz(r-4.7-x86_64)graper_1.29.0.tar.gz(r-4.6-arm64)graper_1.29.0.tar.gz(r-4.6-x86_64)
graper_1.29.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
graper/json (API)
| # Install 'graper' in R: |
| install.packages('graper', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:graper-1.29.0(bioc 3.24)graper-1.28.0(bioc 3.23)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
regressionbayesianclassificationopenblascpp
Last updated from:ba611e23de. Checks:1 WARNING, 11 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | WARNING | 175 | ||
| linux-devel-arm64 | NOTE | 187 | ||
| linux-devel-x86_64 | NOTE | 229 | ||
| source / vignettes | OK | 269 | ||
| linux-release-arm64 | NOTE | 182 | ||
| linux-release-x86_64 | NOTE | 255 | ||
| macos-release-arm64 | NOTE | 129 | ||
| macos-release-x86_64 | NOTE | 214 | ||
| macos-oldrel-arm64 | NOTE | 125 | ||
| macos-oldrel-x86_64 | NOTE | 210 | ||
| windows-devel | NOTE | 148 | ||
| windows-release | NOTE | 169 | ||
| windows-oldrel | NOTE | 145 | ||
| wasm-release | OK | 151 |
Exports:getPIPsgrapermakeExampleDatamakeExampleDataWithUnequalGroupsplotELBOplotGroupPenaltiesplotPosterior
Dependencies:BHclicowplotcpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMatrixmatrixStatsR6RColorBrewerRcppRcppArmadillorlangS7scalesvctrsviridisLitewithr
Vignette illustrating the use of graper in logistic regression
Last update: 2019-06-20
Started: 2018-10-01
Vignette illustrating the use of graper in linear regression
Make example data with four groups | Fit the model | Training diagnostics | Posterior distribtions | Model coefficients and intercept | Posterior inclusion probabilities per feature | Group-wise penalites | Make predictions
Last update: 2019-01-29
Started: 2018-07-13
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Get estimated coefficients from a graper object | coef.graper |
| Get posterior inclusion probabilities per feature | getPIPs |
| Fit a regression model with graper | graper |
| Simulate example data from the graper model | makeExampleData |
| Simulate example data from the graper model with groups of unequal size | makeExampleDataWithUnequalGroups |
| Plot evidence lower bound | plotELBO |
| Plot group-wise penalties | plotGroupPenalties |
| Plot posterior distributions | plotPosterior |
| Predict response on new data | predict.graper |
| Print a graper object | print.graper |
