Man pages for DARTH-git/dampack
Decision-Analytic Modeling Package
| add_common_aes | Adds aesthetics to all plots to reduce code duplication |
| beta_params | Calculate alpha and beta parameters of beta distribution. |
| calc_evpi | Expected Value of Perfect Information (EVPI) |
| calc_evppi | Estimation of the Expected Value of Partial Perfect... |
| calc_evsi | Calculate Expected Value of Sample Information (EVSI) |
| calc_exp_loss | Calculate the expected loss at a range of willingness-to-pay... |
| calculate_icers | Calculate incremental cost-effectiveness ratios (ICERs) |
| calculate_icers_psa | Calculate incremental cost-effectiveness ratios from a 'psa'... |
| calculate_outcome | A function that is used to calculate all outcomes |
| ceac | Cost-Effectiveness Acceptability Curve (CEAC) |
| compute_icers | compute icers for non-dominated strategies |
| create_dsa_oneway | Create one-way deterministic sensitivity analysis object |
| create_dsa_twoway | Create one-way deterministic sensitivity analysis object |
| create_sa | A generic sensitivity analysis object |
| dirichlet_params | Calculate alpha parameters of Dirichlet distribution. |
| example_psa | Sample PSA data for testing |
| example_psa_obj | Sample PSA data for testing |
| gamma_params | Calculate shape and scale (or rate) parameters of a gamma... |
| gen_psa_samp | Generate PSA Sample |
| hund_strat | Sample deterministic data for testing |
| is_owsa | check that object is owsa object |
| labfun | used to automatically label continuous scales |
| lnorm_params | Calculate location and scale parameters of a log-normal... |
| make_param_seq | make a parameter sequence |
| make_psa_obj | Create a PSA object |
| metamodel | Linear regression metamodeling |
| mm_run_reg | Build formula and run linear regression for metamodel |
| number_ticks | Number of ticks for 'ggplot2' plots |
| offset_trans | transformation for owsa_tornado |
| owsa | One-way sensitivity analysis |
| owsa_opt_strat | plot the optimal strategy as the parameter values change |
| owsa_tornado | Tornado plot of a one-way sensitivity analysis |
| plot.ceac | Plot of Cost-Effectiveness Acceptability Curves (CEAC) |
| plot.evpi | Plot of Expected Value of Perfect Information (EVPI) |
| plot.evppi | Plot of Expected Value of Partial Perfect Information (EVPPI) |
| plot.evsi | Plot of Expected Value of Sample Information (EVSI) |
| plot.exp_loss | Plot of Expected Loss Curves (ELC) |
| plot.icers | Plot of ICERs |
| plot.owsa | Plot a sensitivity analysis |
| plot.psa | Plot the psa object |
| plot.twsa | Two-way sensitivity analysis plot |
| predict_ga | Function to compute the preposterior for each of the basis... |
| predict_matrix_tensor_smooth_ga | Predict matrix tensor smooth (GA) |
| predict.metamodel | Predict from a one-way or two-way metamodel |
| predict_smooth_ga | Function to compute the preposterior for each of the basis... |
| print.metamodel | Print metamodel |
| print.sa | print a psa object |
| psa_cdiff | Sample PSA dataset |
| rdirichlet | Random number generation for the Dirichlet distribution with... |
| run_owsa_det | Run deterministic one-way sensitivity analysis (OWSA) |
| run_psa | Calculate outcomes for a PSA using a user-defined function. |
| run_twsa_det | Run deterministic two-way sensitivity analysis (TWSA) |
| summary.ceac | Summarize a ceac |
| summary.metamodel | Summary of metamodel |
| summary.psa | summarize a psa object across all simulations |
| twsa | Two-way sensitivity analysis using linear regression... |
| wrapper_of_user_model | Wrapper function for owsa_det and twsa_det |
