Package: decisionSupport 1.113

decisionSupport: Quantitative Support of Decision Making under Uncertainty

Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.

Authors:Eike Luedeling [cre, aut], Lutz Goehring [aut], Katja Schiffers [aut], Cory Whitney [aut], Eduardo Fernandez [aut]

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decisionSupport.pdf |decisionSupport.html✨
decisionSupport/json (API)

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

Peer review:

Bug tracker:https://github.com/eikeluedeling/decisionsupport/issues

On CRAN:

42 exports 6 stars 1.78 score 150 dependencies 1 mentions 121 scripts 516 downloads

Last updated 5 months agofrom:a7a4370846. Checks:ERROR: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesFAILSep 05 2024
R-4.5-winWARNINGSep 05 2024
R-4.5-linuxWARNINGSep 05 2024
R-4.4-winWARNINGSep 05 2024
R-4.4-macWARNINGSep 05 2024
R-4.3-winWARNINGSep 05 2024
R-4.3-macWARNINGSep 05 2024

Exports:as.estimateas.estimate1dchance_eventcompound_figurecorMatcorMat<-decisionSupportdiscountempirical_EVPIestimateestimate_read_csvestimate_read_csv_oldestimate_write_csvestimate1deviSimulationgompertz_yieldindividualEvpiSimulationmake_CPTmcSimulationmulti_EVPIparamtnormci_fitparamtnormci_numericplainNames2data.frameNamesplot_cashflowplot_distributionsplot_evpiplot_plsplsr.mcSimulationrandomrandom_staterdist90ci_exactrdistq_fitrmvnorm90ci_exactrposnorm90cirtnorm_0_1_90cirtnorm90cisample_CPTsample_simple_CPTscenario_mctemp_situationsvvwelfareDecisionAnalysis

Dependencies:abindaskpassassertthatbackportsbitopsbootbroomcachemcarcarDatacellrangercheckmatechillRchronclassclassIntclicolorspacecorrplotcowplotcpp11crayoncurldata.tabledateDBIDerivdigestdoBydotCall64dplyre1071ecmwfrehaexpmfANCOVAfansifarverfastmapfieldsfilelockFormulaformula.toolsgenericsGenSAgetPassggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehmshttrisobandjsonliteKendallKernSmoothkeyringlabelinglatticelifecyclelme4lmtestlubridatemagrittrmapsMASSMatrixMatrixModelsmc2dmemoisemetRmgcvmicrobenchmarkmimeminqamodelrmsmmunsellmvtnormnleqslvnlmenloptrnnetnumDerivopenssloperator.toolspatchworkpbkrtestpillarpkgconfigplsplyrpolynomprettyunitsprogressproxypurrrquantregR.methodsS3R.ooR.utilsR6rappdirsrasterRColorBrewerRcppRcppEigenRCurlreadxlrematchreshape2rlangRMAWGENrriskDistributionsrstatixrstudioapis2sandwichscalessfsodiumspspamSparseMstringistringrstrucchangesurvivalsysterratibbletidyrtidyselecttimechangetkrplotunitsurcautf8varsvctrsviridisLitewithrwkXMLyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Quantitative Support of Decision Making under Uncertainty.decisionSupport-package
Coerce Monte Carlo simulation results to a data frame.as.data.frame.mcSimulation
simulate occurrence of random eventschance_event
Compound figure for decision supportcompound_figure
Return the Correlation Matrix.corMat
Replace correlation matrix.corMat<-
Welfare Decision and Value of Information Analysis wrapper function.decisionSupport
Discount time series for Net Present Value (NPV) calculationdiscount
Expected value of perfect information (EVPI) for a simple model with the predictor variable sampled from a normal distribution with.empirical_EVPI plot.EVPI_res plot_empirical_EVPI summary.EVPI_res summary_empirical_EVPI
Create a multivariate estimate object.as.estimate estimate
Read an Estimate from CSV - File.estimate_read_csv estimate_read_csv_old
Write an Estimate to CSV - File.estimate_write_csv
Create a 1-dimensional estimate object.as.estimate1d estimate1d
Expected Value of Information (EVI) Simulation.eviSimulation
Gompertz function yield prediction for perennialsgompertz_yield
Plot Histograms of results of an EVI simulationhist.eviSimulation
Plot Histogram of results of a Monte Carlo Simulationhist.mcSimulation
Plot Histogram of results of a Welfare Decision Analysishist.welfareDecisionAnalysis
Individual Expected Value of Perfect Information SimulationindividualEvpiSimulation
Make Conditional Probability tables using the likelihood methodmake_CPT
Perform a Monte Carlo simulation.mcSimulation
Expected value of perfect information (EVPI) for multiple variables. This is a wrapper for the empirical_EVPI function. See the documentation of the 'empirical_EVPI' function for more details.multi_EVPI plot.EVPI_outputs plot_multi_EVPI summary.EVPI_outputs summary_multi_EVPI
Fit parameters of truncated normal distribution based on a confidence interval.paramtnormci_fit
Return parameters of truncated normal distribution based on a confidence interval.paramtnormci_numeric
Transform model function variable names: plain to data.frame names.plainNames2data.frameNames
Cashflow plot for Monte Carlo simulation resultsplot_cashflow
Probability distribution plots for various types of Monte Carlo simulation resultsplot_distributions
Visualizing the results of Expected Value of Perfect Information (EVPI) analysis for various types of Monte Carlo simulation resultsplot_evpi
Visualizing Projection to Latent Structures (PLS) regression outputs for various types of Monte Carlo simulation resultsplot_pls
Partial Least Squares Regression (PLSR) of Monte Carlo simulation results.plsr.mcSimulation
Print Basic Results from Monte Carlo Simulation.print.mcSimulation
Print the Summarized EVI Simulation Results.print.summary.eviSimulation
Print the summary of a Monte Carlo simulation.print.summary.mcSimulation
Print the summarized Welfare Decision Analysis results.print.summary.welfareDecisionAnalysis
Quantiles or empirically based generic random number generation.random random.data.frame random.default random.vector
Draw a random state for a categorical variablerandom_state
Generate random numbers for an estimate.random.estimate
Generate univariate random numbers defined by a 1-d estimate.random.estimate1d
90%-confidence interval based univariate random number generation (by exact parameter calculation).rdist90ci_exact
Quantiles based univariate random number generation (by parameter fitting).rdistq_fit
90%-confidence interval multivariate normal random number generation.rmvnorm90ci_exact
Get and set attributes of an 'estimate' object.corMat.estimate corMat<-.estimate names.estimate row.names.estimate
90%-confidence interval based truncated normal random number generation.rposnorm90ci rtnorm90ci rtnorm_0_1_90ci
Sample a Conditional Probability Tablesample_CPT
Make Conditional Probability tables using the likelihood methodsample_simple_CPT
Perform a Monte Carlo simulation for predefined scenarios.scenario_mc
Sort Summarized EVI Simulation Results..sort.summary.eviSimulation
Summarize EVI Simulation Resultssummary.eviSimulation
Summarize results from Monte Carlo simulation.summary.mcSimulation
Summarize Welfare Decision Analysis results.summary.welfareDecisionAnalysis
Situation occurrence and resolutiontemp_situations
value varier functionvv
Analysis of the underlying welfare based decision problem.welfareDecisionAnalysis