Package: UNCOVER 1.1.0
UNCOVER: Utilising Normalisation Constant Optimisation via Edge Removal (UNCOVER)
Model data with a suspected clustering structure (either in co-variate space, regression space or both) using a Bayesian product model with a logistic regression likelihood. Observations are represented graphically and clusters are formed through various edge removals or additions. Cluster quality is assessed through the log Bayesian evidence of the overall model, which is estimated using either a Sequential Monte Carlo sampler or a suitable transformation of the Bayesian Information Criterion as a fast approximation of the former. The internal Iterated Batch Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is made available as a free standing function.
Authors:
UNCOVER_1.1.0.tar.gz
UNCOVER_1.1.0.zip(r-4.5)UNCOVER_1.1.0.zip(r-4.4)UNCOVER_1.1.0.zip(r-4.3)
UNCOVER_1.1.0.tgz(r-4.4-any)UNCOVER_1.1.0.tgz(r-4.3-any)
UNCOVER_1.1.0.tar.gz(r-4.5-noble)UNCOVER_1.1.0.tar.gz(r-4.4-noble)
UNCOVER_1.1.0.tgz(r-4.4-emscripten)UNCOVER_1.1.0.tgz(r-4.3-emscripten)
UNCOVER.pdf |UNCOVER.html✨
UNCOVER/json (API)
NEWS
| # Install 'UNCOVER' in R: |
| install.packages('UNCOVER', repos = c('https://samuelemerson.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/samuelemerson/uncover/issues
Last updated 1 years agofrom:db79c5a8bb. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 18 2024 |
| R-4.5-win | OK | Sep 18 2024 |
| R-4.5-linux | OK | Sep 18 2024 |
| R-4.4-win | OK | Sep 18 2024 |
| R-4.4-mac | OK | Sep 18 2024 |
| R-4.3-win | OK | Sep 18 2024 |
| R-4.3-mac | OK | Sep 18 2024 |
Exports:IBIS.logregIBIS.logreg.optsplot.IBISplot.UNCOVERpredict.IBISpredict.UNCOVERprint.IBISprint.UNCOVERUNCOVERUNCOVER.opts
Dependencies:abindbackportsBHbitbit64bootbroombroom.helperscachemcarcarDatacardsclicliprcolorspacecorrplotcowplotcpp11crayonDerivdoBydplyrfansifarverfastmapforcatsgenericsGGallyggnewscaleggplot2ggpubrggrepelggsciggsignifggstatsgluegridExtragtablehavenhmsigraphisobandlabelinglabelledlatticelifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkminqamodelrmunsellmvnfastnlmenloptrnnetnumDerivpatchworkpbkrtestpillarpkgconfigplyrpolynomprettyunitsprogresspurrrquantregR6RColorBrewerRcppRcppArmadilloRcppEigenreadrrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Logistic regression iterated batch importance sampling | IBIS.logreg |
| Additional argument generator for 'IBIS.logreg()' | IBIS.logreg.opts |
| Plot various outputs of IBIS | plot.IBIS |
| Plot various outputs of UNCOVER | plot.UNCOVER |
| Prediction method for IBIS | predict.IBIS |
| Prediction method for UNCOVER | predict.UNCOVER |
| Print IBIS | print.IBIS |
| Print UNCOVER | print.UNCOVER |
| Utilising Normalisation Constant Optimisation Via Edge Removal | UNCOVER |
| Additional argument generator for 'UNCOVER()' | UNCOVER.opts |
