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decisionSupport (version 1.105.3)

Quantitative Support of Decision Making under Uncertainty

Description

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.

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Version

Install

install.packages('decisionSupport')

Monthly Downloads

364

Version

1.105.3

License

GPL-3

Maintainer

Eike Luedeling

Last Published

May 12th, 2020

Functions in decisionSupport (1.105.3)

estimate

Create a multivariate estimate object.
estimate1d

Create a 1-dimensional estimate object.
decisionSupport-package

Quantitative Support of Decision Making under Uncertainty.
estimate_read_csv

Read an Estimate from CSV - File.
decisionSupport

Welfare Decision and Value of Information Analysis wrapper function.
estimate_write_csv

Write an Estimate to CSV - File.
random

Quantiles or empirically based generic random number generation.
print.summary.welfareDecisionAnalysis

Print the summarized Welfare Decision Analysis results.
as.data.frame.mcSimulation

Coerce Monte Carlo simulation results to a data frame.
hist.welfareDecisionAnalysis

Plot Histogram of results of a Welfare Decision Analysis
individualEvpiSimulation

Individual Expected Value of Perfect Information Simulation
plsr.mcSimulation

Partial Least Squares Regression (PLSR) of Monte Carlo simulation results.
corMat<-

Replace correlation matrix.
chance_event

simulate occurrence of random events
random.estimate

Generate random numbers for an estimate.
eviSimulation

Expected Value of Information (EVI) Simulation.
random.estimate1d

Generate univariate random numbers defined by a 1-d estimate.
print.mcSimulation

Print Basic Results from Monte Carlo Simulation.
corMat

Return the Correlation Matrix.
gompertz_yield

Gompertz function yield prediction for perennials
row.names.estimate

Get and set attributes of an estimate object.
rtnorm90ci

90%-confidence interval based truncated normal random number generation.
paramtnormci_numeric

Return parameters of truncated normal distribution based on a confidence interval.
hist.mcSimulation

Plot Histogram of results of a Monte Carlo Simulation
plainNames2data.frameNames

Transform model function variable names: plain to data.frame names.
hist.eviSimulation

Plot Histograms of results of an EVI simulation
welfareDecisionAnalysis

Analysis of the underlying welfare based decision problem.
temp_situations

Situation occurrence and resolution
print.summary.eviSimulation

Print the Summarized EVI Simulation Results.
vv

value varier function
rdistq_fit

Quantiles based univariate random number generation (by parameter fitting).
mcSimulation

Perform a Monte Carlo simulation.
discount

Discount time series for Net Present Value (NPV) calculation
summary.mcSimulation

Summarize results from Monte Carlo simulation.
print.summary.mcSimulation

Print the summary of a Monte Carlo simulation.
empirical_EVPI

Expected value of perfect information (EVPI) for a simple model with the predictor variable sampled from a normal distribution with.
sample_CPT

Sample a Conditional Probability Table
make_CPT

Make Conditional Probability tables using the likelihood method
summary.welfareDecisionAnalysis

Summarize Welfare Decision Analysis results.
rmvnorm90ci_exact

90%-confidence interval multivariate normal random number generation.
paramtnormci_fit

Fit parameters of truncated normal distribution based on a confidence interval.
sample_simple_CPT

Make Conditional Probability tables using the likelihood method
rdist90ci_exact

90%-confidence interval based univariate random number generation (by exact parameter calculation).
sort.summary.eviSimulation

Sort Summarized EVI Simulation Results..
summary.eviSimulation

Summarize EVI Simulation Results
random_state

Draw a random state for a categorical variable
multi_EVPI

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.