Causes of Outcome Learning


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Documentation for package ‘CoOL’ version 1.0.3

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CoOL_0_binary_encode_exposure_data Binary encode exposure data
CoOL_0_common_simulation Common example
CoOL_0_complex_simulation Complex example
CoOL_0_confounding_simulation Confounding example
CoOL_0_mediation_simulation Mediation example
CoOL_0_working_example CoOL working example with sex, drug A, and drug B
CoOL_1_initiate_neural_network Initiates a non-negative neural network
CoOL_2_train_neural_network Training the non-negative neural network
CoOL_3_plot_neural_network Plotting the non-negative neural network
CoOL_4_AUC Plot the ROC AUC
CoOL_4_predict_risks Predict the risk of the outcome using the fitted non-negative neural network
CoOL_5_layerwise_relevance_propagation Layer-wise relevance propagation of the fitted non-negative neural network
CoOL_6_dendrogram Dendrogram and sub-groups
CoOL_6_individual_effects_matrix Risk contribution matrix based on individual effects (had all other exposures been set to zero)
CoOL_6_sub_groups Assign sub-groups
CoOL_6_sum_of_individual_effects Predict the risk based on the sum of individual effects
CoOL_7_prevalence_and_mean_risk_plot Prevalence and mean risk plot
CoOL_8_mean_risk_contributions_by_sub_group Mean risk contributions by sub-groups
CoOL_default The default analysis for computational phase of CoOL
cpp_train_network_relu Function used as part of other functions
random Function used as part of other functions
rcpprelu Function used as part of other functions
rcpprelu_neg Function used as part of other functions
relu Function used as part of other functions