Package: flowml 0.1.3
Sebastian Malkusch
flowml: A Backend for a 'nextflow' Pipeline that Performs Machine-Learning-Based Modeling of Biomedical Data
Provides functionality to perform machine-learning-based modeling in a computation pipeline. Its functions contain the basic steps of machine-learning-based knowledge discovery workflows, including model training and optimization, model evaluation, and model testing. To perform these tasks, the package builds heavily on existing machine-learning packages, such as 'caret' <https://github.com/topepo/caret/> and associated packages. The package can train multiple models, optimize model hyperparameters by performing a grid search or a random search, and evaluates model performance by different metrics. Models can be validated either on a test data set, or in case of a small sample size by k-fold cross validation or repeated bootstrapping. It also allows for 0-Hypotheses generation by performing permutation experiments. Additionally, it offers methods of model interpretation and item categorization to identify the most informative features from a high dimensional data space. The functions of this package can easily be integrated into computation pipelines (e.g. 'nextflow' <https://www.nextflow.io/>) and hereby improve scalability, standardization, and re-producibility in the context of machine-learning.
Authors:
flowml_0.1.3.tar.gz
flowml_0.1.3.zip(r-4.5)flowml_0.1.3.zip(r-4.4)flowml_0.1.3.zip(r-4.3)
flowml_0.1.3.tgz(r-4.4-any)flowml_0.1.3.tgz(r-4.3-any)
flowml_0.1.3.tar.gz(r-4.5-noble)flowml_0.1.3.tar.gz(r-4.4-noble)
flowml_0.1.3.tgz(r-4.4-emscripten)flowml_0.1.3.tgz(r-4.3-emscripten)
flowml.pdf |flowml.html✨
flowml/json (API)
# Install 'flowml' in R: |
install.packages('flowml', repos = c('https://boehringer-ingelheim.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/boehringer-ingelheim/flowml/issues
Last updated 10 months agofrom:f77456ed08. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 25 2024 |
R-4.5-win | OK | Oct 25 2024 |
R-4.5-linux | OK | Oct 25 2024 |
R-4.4-win | NOTE | Oct 25 2024 |
R-4.4-mac | NOTE | Oct 25 2024 |
R-4.3-win | NOTE | Oct 25 2024 |
R-4.3-mac | NOTE | Oct 25 2024 |
Exports:create_parserfml_bootstrapfml_examplefml_interpretfml_trainfml_validate
Dependencies:ABCanalysisbitbit64caretclassclicliprclockcodetoolscolorspacecpp11crayondata.tablediagramdigestdplyre1071fansifarverfastshapforeachfurrrfuturefuture.applygenericsgetoptggplot2globalsgluegowergtablehardhathmsipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivoptparseparallellypillarpkgconfigplotrixplyrprettyunitspROCprodlimprogressprogressrproxypurrrR6RColorBrewerRcppRcppArmadilloreadrrecipesreshape2rjsonrlangrpartrsamplescalesshapesliderSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsvipviridisLitevroomwarpwithryardstick
Readme and manuals
Help Manual
Help page | Topics |
---|---|
create_parser | create_parser |
create_resample_experiment | create_resample_experiment |
fml_bootstrap | fml_bootstrap |
fml_example | fml_example |
fml_interpret | fml_interpret |
fml_train | fml_train |
fml_validate | fml_validate |
format_y | format_y |
Resampler | Resampler |
Performs item categorization | run_abc_analysis |