Package: caretMultimodal 1.0.0

Josh Dyce

caretMultimodal: Multimodal Late Fusion with 'caret'

Extends the 'caret' framework to support late fusion workflows, enabling users to train models independently across multiple data modalities and combine their predictions into a single meta-model. Designed for developers, data scientists, and biomedical researchers alike, 'caretMultimodal' aims to make late fusion ensemble modelling as accessible and flexible as single-dataset workflows in 'caret'. Late fusion methods are based on Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1>.

Authors:Josh Dyce [aut, cre], Amrit Singh [aut]

caretMultimodal_1.0.0.tar.gz
caretMultimodal_1.0.0.tar.gz(r-4.7-any)caretMultimodal_1.0.0.tar.gz(r-4.6-any)
caretMultimodal_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
caretMultimodal/json (API)

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

Bug tracker:https://github.com/compbio-lab/caretmultimodal/issues

Pkgdown/docs site:https://compbio-lab.github.io

Datasets:

On CRAN:

Conda:

1.70 score 13 exports 93 dependencies

Last updated from:7d7ceb9bee. Checks:2 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE189
source / vignettesOK219
linux-release-x86_64NOTE179
wasm-releaseOK170

Exports:caret_listcaret_stackcompute_ablationcompute_feature_contributionscompute_metriccompute_model_contributionsoof_predictionsplot_ablationplot_feature_contributionsplot_metricplot_model_contributionsplot_rocprepare_mae

Dependencies:abindBiobaseBiocBaseUtilsBiocGenericscaretclasscliclockcodetoolscpp11data.tableDelayedArraydiagramdigestdplyre1071farverforeachfuturefuture.applygenericsGenomicRangesggplot2glmnetglobalsgluegowergridExtragtablehardhatipredIRangesisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsModelMetricsMultiAssayExperimentnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartS4ArraysS4VectorsS7scalesSeqinfoshapeSparseArraysparsevctrsSQUAREMstringistringrSummarizedExperimentsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisviridisLitewithrXVector

Readme and manuals

Help Manual

Help pageTopics
Construct a 'caret_list' objectcaret_list
Construct a 'caret_stack' object.caret_stack
Perform an ablation analysis for a 'caret_stack'compute_ablation.caret_stack
Compute the feature level contributions for a 'caret_stack'.compute_feature_contributions.caret_stack
Compute metrics with a provided metric functioncompute_metric.caret_stack
Compute the relative contributions of each of the base models in the ensemble modelcompute_model_contributions.caret_stack
Heart Failure Datasetsheart_failure_datasets
Pre-trained 'caret_stack' on Heart Failure Datasetsheart_failure_stack
Out-of-fold predictions from a 'caret_list'oof_predictions.caret_list
Out-of-fold predictions from a caret_stackoof_predictions.caret_stack
Make a bar plot of an ablation analysis for a 'caret_stack'.plot_ablation.caret_stack
Make a bar plot of feature level for a 'caret_stack'.plot_feature_contributions.caret_stack
Plot metrics computed with a provided metric functionplot_metric.caret_stack
Plot the relative contributions of each of the base models in the ensemble modelplot_model_contributions.caret_stack
Plot ROC curves for individual and ensemble models in a caret_stackplot_roc.caret_stack
Predict from a 'caret_list'predict.caret_list
Create a matrix of predictions for a 'caret_stack' object.predict.caret_stack
Prepare a MultiAssayExperiment for use with caretMultimodalprepare_mae
Provide a summary of the best tuning parameters and resampling metrics for all the 'caret_list' models.summary.caret_list
Get a summary of a 'caret_stack' objectsummary.caret_stack