R: Decision-Analytic Modeling Package

# Decision-Analytic Modeling Package

## Documentation for package ‘dampack’ version 1.0.2.1000

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

beta_params | Calculate alpha and beta parameters of beta distribution.
calculate_icers | Calculate incremental cost-effectiveness ratios (ICERs)
calculate_icers_psa | Calculate incremental cost-effectiveness ratios from a 'psa' object.
calc_evpi | Expected Value of Perfect Information (EVPI)
calc_evppi | Estimation of the Expected Value of Partial Perfect Information (EVPPI) using a linear regression metamodel approach
calc_evsi | Calculate Expected Value of Sample Information (EVSI)
calc_exp_loss | Calculate the expected loss at a range of willingness-to-pay thresholds
ceac | Cost-Effectiveness Acceptability Curve (CEAC)
create_dsa_oneway | Create one-way deterministic sensitivity analysis object
create_dsa_twoway | Create one-way deterministic 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 distribution.
gen_psa_samp | Generate PSA Sample
hund_strat | Sample deterministic data for testing
lnorm_params | Calculate location and scale parameters of a log-normal distribution.
make_psa_obj | Create a PSA object
metamodel | Linear regression metamodeling
number_ticks | Number of ticks for 'ggplot2' plots
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.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.metamodel | Predict from a one-way or two-way metamodel
print.metamodel | Print metamodel
print.sa | print a psa object
psa_cdiff | Sample PSA dataset
rdirichlet | Random number generation for the Dirichlet distribution with parameter vector alpha.
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.metamodel | Summary of metamodel
summary.psa | summarize a psa object across all simulations
twsa | Two-way sensitivity analysis using linear regression metamodeling