Package 'voiageR'

Calculate Expected Value of Perfect Information

Description

Calculates Expected Value of Perfect Information using the voiage Python library.

Usage

evpi(net_benefits, population = NULL, time_horizon = NULL,
  discount_rate = NULL)

Arguments

net_benefits	A matrix or data frame with PSA samples in rows and strategies in columns.
population	Optional population size for scaling.
time_horizon	Optional time horizon for scaling.
discount_rate	Optional discount rate for scaling.

Value

The calculated EVPI value.

Examples

## Not run: 
net_benefits <- matrix(rnorm(2000), nrow = 1000, ncol = 2)
evpi(net_benefits)

## End(Not run)

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Calculate Expected Value of Partial Perfect Information

Description

Calculates Expected Value of Partial Perfect Information using the voiage Python library.

Usage

evppi(net_benefits, parameter_samples,
  parameters_of_interest = NULL, population = NULL, time_horizon = NULL,
  discount_rate = NULL)

Arguments

net_benefits	A matrix or data frame with PSA samples in rows and strategies in columns.
parameter_samples	A named list or data frame with parameter samples.
parameters_of_interest	Optional character vector of parameter names to retain. Defaults to all named parameters in parameter_samples.
population	Optional population size for scaling.
time_horizon	Optional time horizon for scaling.
discount_rate	Optional discount rate for scaling.

Value

The calculated EVPPI value.

Examples

## Not run: 
net_benefits <- matrix(rnorm(2000), nrow = 1000, ncol = 2)
param_samples <- list(effect = rnorm(1000), cost = rnorm(1000))
evppi(net_benefits, param_samples)

## End(Not run)

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Calculate Expected Value of Sample Information

Description

Calculates Expected Value of Sample Information using the voiage Python library.

Usage

evsi(model_func, prior_samples, trial_design, population = NULL, time_horizon = NULL,
  discount_rate = NULL, method = c("two_loop", "regression", "efficient",
  "moment_based"), n_outer_loops = 100, n_inner_loops = 1000, metamodel = "linear")

Arguments

model_func	A function that takes parameter samples and returns net benefits.
prior_samples	A named list or data frame with prior parameter samples.
trial_design	A list of trial arm specifications.
population	Optional population size for scaling.
time_horizon	Optional time horizon for scaling.
discount_rate	Optional discount rate for scaling.
method	EVSI approximation method.
n_outer_loops	Number of outer Monte Carlo loops.
n_inner_loops	Number of inner Monte Carlo loops.
metamodel	Metamodel used for the efficient approximation.

Value

The calculated EVSI value.

Examples

## Not run: 
model_func <- function(params) {
  cbind(params$param1, params$param2)
}
prior_samples <- list(param1 = rnorm(1000), param2 = rnorm(1000))
trial_design <- list(
  arms = list(
    list(name = "Treatment", sample_size = 50),
    list(name = "Control", sample_size = 50)
  )
)
evsi(model_func, prior_samples, trial_design, method = "efficient")

## End(Not run)

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Initialize the voiage Python Module

Description

Imports and caches the voiage Python module through reticulate.

Usage

init_voiage()

Value

Invisibly returns NULL.

Examples

## Not run: 
init_voiage()

## End(Not run)

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Check Whether voiage Is Available

Description

Checks whether the voiage Python package is available to reticulate.

Usage

is_voiage_available()

Value

A logical value indicating whether the Python voiage package can be imported.

Examples

## Not run: 
is_voiage_available()

## End(Not run)

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Set the Python Environment for voiage

Description

Configures the Python environment reticulate should use before importing voiage.

Usage

set_voiage_env(env, type = c("virtualenv", "conda"))

Arguments

env	The virtualenv or conda environment name or path.
type	The environment type, either "virtualenv" or "conda".

Value

Invisibly returns NULL.

Examples

## Not run: 
set_voiage_env("myenv", type = "virtualenv")
set_voiage_env("myenv", type = "conda")

## End(Not run)

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voiageR: An R Interface to the voiage Python Library

Description

An R interface to the voiage Python library for Value of Information analysis, with helper functions for Python environment management and availability checks via reticulate.

Details

The package exports init_voiage, evpi, evppi, evsi, is_voiage_available, and set_voiage_env.

Documentation policy:

• The release ships a PDF reference manual built from the package help pages.

• The release also ships a narrative vignette as the long-form companion guide.

Author(s)

voiage Development Team

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