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:Sebastian Malkusch [aut, cre], Kolja Becker [aut], Alexander Peltzer [ctb], Neslihan Kaya [ctb], Boehringer Ingelheim Ltd. [cph, fnd]

flowml_0.1.3.tar.gz
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flowml_0.1.3.tgz(r-4.4-any)flowml_0.1.3.tgz(r-4.3-any)
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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'))

Peer review:

Bug tracker:https://github.com/boehringer-ingelheim/flowml/issues

On CRAN:

2.00 score 219 downloads 6 exports 97 dependencies

Last updated 10 months agofrom:f77456ed08. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-winOKOct 25 2024
R-4.5-linuxOKOct 25 2024
R-4.4-winNOTEOct 25 2024
R-4.4-macNOTEOct 25 2024
R-4.3-winNOTEOct 25 2024
R-4.3-macNOTEOct 25 2024

Exports:create_parserfml_bootstrapfml_examplefml_interpretfml_trainfml_validate

Dependencies:ABCanalysisbitbit64caretclassclicliprclockcodetoolscolorspacecpp11crayondata.tablediagramdigestdplyre1071fansifarverfastshapforeachfurrrfuturefuture.applygenericsgetoptggplot2globalsgluegowergtablehardhathmsipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivoptparseparallellypillarpkgconfigplotrixplyrprettyunitspROCprodlimprogressprogressrproxypurrrR6RColorBrewerRcppRcppArmadilloreadrrecipesreshape2rjsonrlangrpartrsamplescalesshapesliderSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsvipviridisLitevroomwarpwithryardstick

Readme and manuals

Help Manual

Help pageTopics
create_parsercreate_parser
create_resample_experimentcreate_resample_experiment
fml_bootstrapfml_bootstrap
fml_examplefml_example
fml_interpretfml_interpret
fml_trainfml_train
fml_validatefml_validate
format_yformat_y
ResamplerResampler
Performs item categorizationrun_abc_analysis