Function reference
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calculate_ceac() - Calculate cost-effectiveness probabilities.
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calculate_nb() - Calculate NMB and NHB.
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check_binary() - Check binary
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check_mean_qol() - Check mean quality of life
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check_positive() - Check whether variable is strictly positive
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check_range() - Check range
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check_sum_probs() - Check sum probabilities
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check_sum_vars() - Check sum variables
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check_surv_mod() - Check parametric survival models
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df_pa - Dataframe for testing
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do_check() - Check range
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do_discount_check() - Perform discounted and undiscounted results check
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do_quick_check() - Perform quick checks of inputs and outputs
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dsa_lm_metamodel() - Perform DSA using linear metamodel
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estimate_decision_sensitivity() - Estimate decision sensitivy DSA using linear metamodel
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fit_dist() - Fit distribution to parameter
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fit_lm_metamodel() - Fit linear metamodel
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generate_cor() - Generate correlation matrix
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generate_det_inputs() - Generate deterministic model inputs.
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generate_pa_inputs() - Generate probabilistic model inputs.
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generate_pa_inputs_psm() - Generate probabilistic model inputs for partitioned survival model.
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generate_sum_stats() - Generate summary statistics
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perform_dowsa() - Perform deterministic one-way sensitivity analyses using probabilistic inputs and outputs.
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perform_simulation() - Perform the health economic simulation.
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perform_simulation_psm() - Perform the health economic simulation using partitioned survival model.
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plot_ce() - Plot cost-effectiveness plane.
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plot_ceac() - Plot cost-effectiveness acceptability curves.
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plot_convergence() - Plot moving average
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plot_ice() - Plotting the incremental cost-effectiveness plane.
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plot_nb() - Plot (i)NMB or (i)NHB.
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plot_surv_mod() - Plot parametric survival models
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plot_tornado() - Plot results of DSA in a Tornado diagram
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predict_metamodel() - Predict using a fitted metamodel
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summary_ice() - Summary statistics of the incremental cost-effectiveness plane.
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vis_1_param() - Visualise the distribution of a single parameter
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vis_2_params() - Visualise the distribution of two parameters