Package: CoOL 1.1.2

CoOL: Causes of Outcome Learning

Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional 'ggtree' package can be obtained through Bioconductor.

Authors:Andreas Rieckmann [aut, cre], Piotr Dworzynski [aut], Leila Arras [ctb], Claus Thorn Ekstrom [aut]

CoOL_1.1.2.tar.gz
CoOL_1.1.2.zip(r-4.5)CoOL_1.1.2.zip(r-4.4)CoOL_1.1.2.zip(r-4.3)
CoOL_1.1.2.tgz(r-4.4-x86_64)CoOL_1.1.2.tgz(r-4.4-arm64)CoOL_1.1.2.tgz(r-4.3-x86_64)CoOL_1.1.2.tgz(r-4.3-arm64)
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CoOL.pdf |CoOL.html✨
CoOL/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 6 scripts 296 downloads 1 mentions 28 exports 51 dependencies

Last updated 3 years agofrom:39bc59ed97. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-win-x86_64NOTEOct 25 2024
R-4.5-linux-x86_64NOTEOct 25 2024
R-4.4-win-x86_64NOTEOct 25 2024
R-4.4-mac-x86_64NOTEOct 25 2024
R-4.4-mac-aarch64NOTEOct 25 2024
R-4.3-win-x86_64NOTEOct 25 2024
R-4.3-mac-x86_64NOTEOct 25 2024
R-4.3-mac-aarch64NOTEOct 25 2024

Exports:CoOL_0_binary_encode_exposure_dataCoOL_0_common_simulationCoOL_0_complex_simulationCoOL_0_confounding_simulationCoOL_0_mediation_simulationCoOL_0_working_exampleCoOL_1_initiate_neural_networkCoOL_2_train_neural_networkCoOL_3_plot_neural_networkCoOL_4_AUCCoOL_4_predict_risksCoOL_5_layerwise_relevance_propagationCoOL_6_calibration_plotCoOL_6_dendrogramCoOL_6_individual_effects_matrixCoOL_6_number_of_sub_groupsCoOL_6_sub_groupsCoOL_6_sum_of_individual_effectsCoOL_7_prevalence_and_mean_risk_plotCoOL_8_mean_risk_contributions_by_sub_groupCoOL_9_visualised_mean_risk_contributionsCoOL_9_visualised_mean_risk_contributions_legendCoOL_defaultcpp_train_network_relurandomrcpprelurcpprelu_negrelu

Dependencies:bootclassclassIntcliClustGeocolorspacedata.tableDBIdeldire1071fansifarverggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmltoolsmunsellnlmepillarpkgconfigplyrpROCproxyR6RColorBrewerRcppRcppArmadillorlangs2scalessfspspDataspdeptibbleunitsutf8vctrsviridisLitewesandersonwithrwk

Readme and manuals

Help Manual

Help pageTopics
Binary encode exposure dataCoOL_0_binary_encode_exposure_data
Common exampleCoOL_0_common_simulation
Complex exampleCoOL_0_complex_simulation
Confounding exampleCoOL_0_confounding_simulation
Mediation exampleCoOL_0_mediation_simulation
CoOL working example with sex, drug A, and drug BCoOL_0_working_example
Initiates a non-negative neural networkCoOL_1_initiate_neural_network
Training the non-negative neural networkCoOL_2_train_neural_network
Plotting the non-negative neural networkCoOL_3_plot_neural_network
Plot the ROC AUCCoOL_4_AUC
Predict the risk of the outcome using the fitted non-negative neural networkCoOL_4_predict_risks
Layer-wise relevance propagation of the fitted non-negative neural networkCoOL_5_layerwise_relevance_propagation
Calibration curveCoOL_6_calibration_plot
Dendrogram and sub-groupsCoOL_6_dendrogram
Risk contribution matrix based on individual effects (had all other exposures been set to zero)CoOL_6_individual_effects_matrix
Number of subgroupsCoOL_6_number_of_sub_groups
Assign sub-groupsCoOL_6_sub_groups
Predict the risk based on the sum of individual effectsCoOL_6_sum_of_individual_effects
Prevalence and mean risk plotCoOL_7_prevalence_and_mean_risk_plot
Mean risk contributions by sub-groupsCoOL_8_mean_risk_contributions_by_sub_group
Visualisation of the mean risk contributions by sub-groupsCoOL_9_visualised_mean_risk_contributions
Legend to the visualisation of the mean risk contributions by sub-groupsCoOL_9_visualised_mean_risk_contributions_legend
The default analysis for computational phase of CoOLCoOL_default
Function used as part of other functionscpp_train_network_relu
Function used as part of other functionsrandom
Function used as part of other functionsrcpprelu
Function used as part of other functionsrcpprelu_neg
Function used as part of other functionsrelu