Package: dampack 1.0.2.1000

David Garibay

dampack: Decision-Analytic Modeling Package

A suite of functions for analyzing and visualizing the health economic outputs of mathematical models. This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the National Institutes of Health under award no. R01AI138783. The content of this package is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The theoretical underpinnings of 'dampack''s functionality are detailed in Hunink et al. (2014) <doi:10.1017/CBO9781139506779>.

Authors:Fernando Alarid-Escudero [aut], Greg Knowlton [aut], Caleb W. Easterly [aut], David Garibay [ctb, cre], Mark Clements [ctb], Eva Enns [aut]

dampack_1.0.2.1000.tar.gz
dampack_1.0.2.1000.zip(r-4.7)dampack_1.0.2.1000.zip(r-4.6)dampack_1.0.2.1000.zip(r-4.5)
dampack_1.0.2.1000.tgz(r-4.6-any)dampack_1.0.2.1000.tgz(r-4.5-any)
dampack_1.0.2.1000.tar.gz(r-4.7-any)dampack_1.0.2.1000.tar.gz(r-4.6-any)
dampack_1.0.2.1000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dampack/json (API)

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

Bug tracker:https://github.com/darth-git/dampack/issues

Datasets:

On CRAN:

Conda:

8.23 score 41 stars 207 scripts 659 downloads 24 exports 39 dependencies

Last updated from:0430fee8b8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK180
source / vignettesOK250
linux-release-x86_64OK209
macos-release-arm64OK193
macos-oldrel-arm64OK172
windows-develOK164
windows-releaseOK137
windows-oldrelOK155
wasm-releaseOK117

Exports:beta_paramscalc_evpicalc_evppicalc_evsicalc_exp_losscalculate_icerscalculate_icers_psaceaccreate_dsa_onewaycreate_dsa_twowaydirichlet_paramsgamma_paramsgen_psa_samplnorm_paramsmake_psa_objmetamodelnumber_ticksowsaowsa_opt_stratowsa_tornadorun_owsa_detrun_psarun_twsa_dettwsa

Dependencies:bootclicpp11dplyrellipsefarvergenericsggplot2ggrepelgluegtableisobandlabelinglatticelifecyclemagrittrMatrixmgcvnlmepillarpkgconfigpurrrR6RColorBrewerRcpprlangS7scalesstringistringrtibbletidyrtidyselecttriangletruncnormutf8vctrsviridisLitewithr

Basic Cost Effectiveness Analysis
Background | Example 1: CEA using average cost and effectiveness of HIV Screening strategies in the US | Data | Calculate ICERs | Plot CEA results | Example 2: CEA using a probabilistic sensitivity analysis of treatment strategies for Clostridioides difficile infection
Rendered frombasic_cea.Rmdusingknitr::rmarkdown

Last update: 2024-09-13
Started: 2019-10-02

Deterministic Sensitivity Analysis: Generation
Overview | Decision Model Format | Generating DSA results | One-way DSA generation | Two-way DSA Generation
Rendered fromdsa_generation.Rmdusingknitr::rmarkdown

Last update: 2024-09-13
Started: 2019-09-26

Probabilistic Sensitivity Analysis: Analysis
Overview | Probability Sensitivity Analysis: An Introduction | PSA in dampack | Read in data | Cost-effectiveness Acceptability Curve | Expected Loss Curve | One-way Sensitivity Analysis | Tornado Plot | Optimal Strategy Plot | Two-way Sensitivty Analysis
Rendered frompsa_analysis.Rmdusingknitr::rmarkdown

Last update: 2024-09-13
Started: 2019-09-26

Probabilistic Sensitivity Analysis: Generation
Overview | Decision Model Format | Generating Parameter Samples for a PSA | Generating Outcomes for the PSA | Creating a Fully-functional PSA Object
Rendered frompsa_generation.Rmdusingknitr::rmarkdown

Last update: 2024-09-13
Started: 2019-09-26

Value of Information Analysis
Overview | EVPI | Metamodeling VoI Functions | EVPPI | EVSI | Metamodeling Appendix
Rendered fromvoi.Rmdusingknitr::rmarkdown

Last update: 2024-09-13
Started: 2019-09-26

Readme and manuals

Help Manual

Help pageTopics
Calculate alpha and beta parameters of beta distribution.beta_params
Expected Value of Perfect Information (EVPI)calc_evpi
Estimation of the Expected Value of Partial Perfect Information (EVPPI) using a linear regression metamodel approachcalc_evppi
Calculate Expected Value of Sample Information (EVSI)calc_evsi
Calculate the expected loss at a range of willingness-to-pay thresholdscalc_exp_loss
Calculate incremental cost-effectiveness ratios (ICERs)calculate_icers
Calculate incremental cost-effectiveness ratios from a 'psa' object.calculate_icers_psa
Cost-Effectiveness Acceptability Curve (CEAC)ceac
Create one-way deterministic sensitivity analysis objectcreate_dsa_oneway
Create one-way deterministic sensitivity analysis objectcreate_dsa_twoway
Calculate alpha parameters of Dirichlet distribution.dirichlet_params
Sample PSA data for testingexample_psa
Sample PSA data for testingexample_psa_obj
Calculate shape and scale (or rate) parameters of a gamma distribution.gamma_params
Generate PSA Samplegen_psa_samp
Sample deterministic data for testinghund_strat
Calculate location and scale parameters of a log-normal distribution.lnorm_params
Create a PSA objectmake_psa_obj
Linear regression metamodelingmetamodel
Number of ticks for 'ggplot2' plotsnumber_ticks
One-way sensitivity analysisowsa
plot the optimal strategy as the parameter values changeowsa_opt_strat
Tornado plot of a one-way sensitivity analysisowsa_tornado
Plot of Expected Value of Perfect Information (EVPI)plot.evpi
Plot of Expected Value of Partial Perfect Information (EVPPI)plot.evppi
Plot of Expected Value of Sample Information (EVSI)plot.evsi
Plot of Expected Loss Curves (ELC)plot.exp_loss
Plot of ICERsplot.icers
Plot a sensitivity analysisplot.owsa
Plot the psa objectplot.psa
Two-way sensitivity analysis plotplot.twsa
Predict from a one-way or two-way metamodelpredict.metamodel
Print metamodelprint.metamodel
print a psa objectprint.sa
Sample PSA datasetpsa_cdiff
Random number generation for the Dirichlet distribution with parameter vector alpha.rdirichlet
Run deterministic one-way sensitivity analysis (OWSA)run_owsa_det
Calculate outcomes for a PSA using a user-defined function.run_psa
Run deterministic two-way sensitivity analysis (TWSA)run_twsa_det
Summary of metamodelsummary.metamodel
summarize a psa object across all simulationssummary.psa
Two-way sensitivity analysis using linear regression metamodelingtwsa