R/distributions.R
In albgarre/biorisk: What the Package Does (One Line, Title Case)
#' @title Normal Class
#'
#' @description
#' An element for a normal distribution
#'
#' @importFrom tibble tibble
#'
#' @export
#'
Normal <- R6::R6Class(
classname = "Normal",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("mu", "sigma"),
input_types = list(mu = "continuous",
sigma = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
self$depends_on$mu$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rnorm(niter, mu, sigma)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rnorm(niter0, mu, sigma)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title LnNormal Class
#'
#' @description
#' An element for a log-normal distribution (base e)
#'
#' @export
#'
LnNormal <- R6::R6Class(
classname = "LnNormal",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("mu_ln", "sigma_ln"),
input_types = list(mu_ln = "continuous",
sigma_ln = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
exp(self$depends_on$mu_ln$point_estimate())
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rlnorm(niter, mu_ln, sigma_ln)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rlnorm(niter0, mu_ln, sigma_ln)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title LogNormal Class
#'
#' @description
#' An element for a log-normal distribution (base 10)
#'
#' @export
#'
LogNormal <- R6::R6Class(
classname = "LogNormal",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("mu_log10", "sigma_log10"),
input_types = list(mu_log10 = "continuous",
sigma_log10 = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
10^(self$depends_on$mu_log10$point_estimate())
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
y = rnorm(niter, mu_log10, sigma_log10),
x = 10^y
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
y = rnorm(niter0, mu_log10, sigma_log10),
x = 10^y
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Weibull Class
#'
#' @description
#' An element for a Weibull distribution
#'
#' @export
#'
Weibull <- R6::R6Class(
classname = "Weibull",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("shape", "scale"),
input_types = list(shape = "continuous",
scale = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
self$depends_on$scale$point_estimate()*log(2)^(1/self$depends_on$shape$point_estimate())
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rweibull(niter, shape, scale)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rweibull(niter0, shape, scale)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Beta Class
#'
#' @description
#' An element for a beta distribution
#'
#' @export
#'
Beta <- R6::R6Class(
classname = "Beta",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("shape1", "shape2"),
input_types = list(shape1 = "continuous",
shape2 = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
a <- self$depends_on$shape1$point_estimate()
b <- self$depends_on$shape2$point_estimate()
a/(a+b)
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rbeta(niter, shape1, shape2)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rbeta(niter0, shape1, shape2)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Gamma Class
#'
#' @description
#' An element for a gamma distribution
#'
#' @export
#'
Gamma <- R6::R6Class(
classname = "Gamma",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("shape", "scale"),
input_types = list(shape = "continuous",
scale = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
self$depends_on$shape$point_estimate() * self$depends_on$scale$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rgamma(niter, shape, scale = scale)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rgamma(niter0, shape, scale = scale)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Exponential Class
#'
#' @description
#' An element for a exponential distribution
#'
#' @export
#'
Exponential <- R6::R6Class(
classname = "Exponential",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("rate"),
input_types = list(rate = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
log(2)/self$depends_on$rate$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rexp(niter, rate)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rexp(niter0, rate)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Uniform Class
#'
#' @description
#' An element for a uniform distribution
#'
#' @export
#'
Uniform <- R6::R6Class(
classname = "Uniform",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("min", "max"),
input_types = list(min = "continuous",
max = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
(self$depends_on$min$point_estimate() + self$depends_on$max$point_estimate())/2
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = runif(niter, min, max)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = runif(niter0, min, max)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Triangular Class
#'
#' @description
#' An element for a triangular distribution
#'
#' @export
#'
Triangular <- R6::R6Class(
classname = "Triangular",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("a", "b", "c"),
input_types = list(a = "continuous",
b = "continuous",
c = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
(self$depends_on$a$point_estimate() + self$depends_on$b$point_estimate() + self$depends_on$c$point_estimate() )/3
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = extraDistr::rtriang(niter, a, b, c)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = extraDistr::rtriang(niter0, a, b, c)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title TriangularSym Class
#'
#' @description
#' An element for a symmetrical triangular distribution
#'
#' @export
#'
TriangularSym <- R6::R6Class(
classname = "TriangularSym",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("a", "b"),
input_types = list(a = "continuous",
b = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
(self$depends_on$a$point_estimate() * self$depends_on$b$point_estimate())/2
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = extraDistr::rtriang(niter, a, b)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = extraDistr::rtriang(niter0, a, b)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Pareto Class
#'
#' @description
#' An element for a Pareto distribution
#'
#' @export
#'
Pareto <- R6::R6Class(
classname = "Pareto",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("a", "b"),
input_types = list(a = "continuous",
b = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the model
#'
point_estimate = function() {
self$depends_on$b$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = extraDistr::rpareto(niter, a, b)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = extraDistr::rpareto(niter0, a, b)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Pert Class
#'
#' @description
#' An element for a Pert distribution
#'
#' @export
#'
Pert <- R6::R6Class(
classname = "Pert",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("min", "mode", "max", "shape"),
input_types = list(min = "continuous",
mode = "continuous",
max = "continuous",
shape = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the mode
#'
point_estimate = function() {
self$depends_on$mode$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = mc2d::rpert(niter, min, mode, max, shape)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = mc2d::rpert(niter, min, mode, max, shape)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title LogisticDistr Class
#'
#' @description
#' An element for a Logistic distribution
#'
#' @export
#'
LogisticDistr <- R6::R6Class(
classname = "LogisticDistr",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("location", "scale"),
input_types = list(location = "continuous",
scale = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the mode
#'
point_estimate = function() {
self$depends_on$location$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rlogis(niter, location, scale)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rlogis(niter, location, scale)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
albgarre/biorisk documentation built on Feb. 23, 2025, 5:19 a.m.
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#' @title Normal Class
#'
#' @description
#' An element for a normal distribution
#'
#' @importFrom tibble tibble
#'
#' @export
#'
Normal <- R6::R6Class(
classname = "Normal",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("mu", "sigma"),
input_types = list(mu = "continuous",
sigma = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
self$depends_on$mu$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rnorm(niter, mu, sigma)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rnorm(niter0, mu, sigma)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title LnNormal Class
#'
#' @description
#' An element for a log-normal distribution (base e)
#'
#' @export
#'
LnNormal <- R6::R6Class(
classname = "LnNormal",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("mu_ln", "sigma_ln"),
input_types = list(mu_ln = "continuous",
sigma_ln = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
exp(self$depends_on$mu_ln$point_estimate())
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rlnorm(niter, mu_ln, sigma_ln)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rlnorm(niter0, mu_ln, sigma_ln)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title LogNormal Class
#'
#' @description
#' An element for a log-normal distribution (base 10)
#'
#' @export
#'
LogNormal <- R6::R6Class(
classname = "LogNormal",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("mu_log10", "sigma_log10"),
input_types = list(mu_log10 = "continuous",
sigma_log10 = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
10^(self$depends_on$mu_log10$point_estimate())
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
y = rnorm(niter, mu_log10, sigma_log10),
x = 10^y
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
y = rnorm(niter0, mu_log10, sigma_log10),
x = 10^y
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Weibull Class
#'
#' @description
#' An element for a Weibull distribution
#'
#' @export
#'
Weibull <- R6::R6Class(
classname = "Weibull",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("shape", "scale"),
input_types = list(shape = "continuous",
scale = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
self$depends_on$scale$point_estimate()*log(2)^(1/self$depends_on$shape$point_estimate())
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rweibull(niter, shape, scale)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rweibull(niter0, shape, scale)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Beta Class
#'
#' @description
#' An element for a beta distribution
#'
#' @export
#'
Beta <- R6::R6Class(
classname = "Beta",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("shape1", "shape2"),
input_types = list(shape1 = "continuous",
shape2 = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
a <- self$depends_on$shape1$point_estimate()
b <- self$depends_on$shape2$point_estimate()
a/(a+b)
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rbeta(niter, shape1, shape2)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rbeta(niter0, shape1, shape2)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Gamma Class
#'
#' @description
#' An element for a gamma distribution
#'
#' @export
#'
Gamma <- R6::R6Class(
classname = "Gamma",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("shape", "scale"),
input_types = list(shape = "continuous",
scale = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
self$depends_on$shape$point_estimate() * self$depends_on$scale$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rgamma(niter, shape, scale = scale)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rgamma(niter0, shape, scale = scale)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Exponential Class
#'
#' @description
#' An element for a exponential distribution
#'
#' @export
#'
Exponential <- R6::R6Class(
classname = "Exponential",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("rate"),
input_types = list(rate = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the median
#'
point_estimate = function() {
log(2)/self$depends_on$rate$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rexp(niter, rate)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rexp(niter0, rate)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Uniform Class
#'
#' @description
#' An element for a uniform distribution
#'
#' @export
#'
Uniform <- R6::R6Class(
classname = "Uniform",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("min", "max"),
input_types = list(min = "continuous",
max = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
(self$depends_on$min$point_estimate() + self$depends_on$max$point_estimate())/2
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = runif(niter, min, max)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = runif(niter0, min, max)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Triangular Class
#'
#' @description
#' An element for a triangular distribution
#'
#' @export
#'
Triangular <- R6::R6Class(
classname = "Triangular",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("a", "b", "c"),
input_types = list(a = "continuous",
b = "continuous",
c = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
(self$depends_on$a$point_estimate() + self$depends_on$b$point_estimate() + self$depends_on$c$point_estimate() )/3
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = extraDistr::rtriang(niter, a, b, c)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = extraDistr::rtriang(niter0, a, b, c)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title TriangularSym Class
#'
#' @description
#' An element for a symmetrical triangular distribution
#'
#' @export
#'
TriangularSym <- R6::R6Class(
classname = "TriangularSym",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("a", "b"),
input_types = list(a = "continuous",
b = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the expected value
#'
point_estimate = function() {
(self$depends_on$a$point_estimate() * self$depends_on$b$point_estimate())/2
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = extraDistr::rtriang(niter, a, b)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = extraDistr::rtriang(niter0, a, b)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Pareto Class
#'
#' @description
#' An element for a Pareto distribution
#'
#' @export
#'
Pareto <- R6::R6Class(
classname = "Pareto",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("a", "b"),
input_types = list(a = "continuous",
b = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the model
#'
point_estimate = function() {
self$depends_on$b$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = extraDistr::rpareto(niter, a, b)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = extraDistr::rpareto(niter0, a, b)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title Pert Class
#'
#' @description
#' An element for a Pert distribution
#'
#' @export
#'
Pert <- R6::R6Class(
classname = "Pert",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("min", "mode", "max", "shape"),
input_types = list(min = "continuous",
mode = "continuous",
max = "continuous",
shape = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the mode
#'
point_estimate = function() {
self$depends_on$mode$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = mc2d::rpert(niter, min, mode, max, shape)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = mc2d::rpert(niter, min, mode, max, shape)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
#' @title LogisticDistr Class
#'
#' @description
#' An element for a Logistic distribution
#'
#' @export
#'
LogisticDistr <- R6::R6Class(
classname = "LogisticDistr",
inherit = ContinuousElement,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
#'
#' @param name A character defining the name for the element
#' @param units A character vector of units for each input
#' @param output_unit A character with the unit of the output
#' @param level Level of the distribution (for 2D Monte Carlo). By default, `0`
#'
#' @return A new instance of the element
#'
initialize = function(name,
units = NA,
output_unit = NA,
level = 0) {
super$initialize(name,
input_names = c("location", "scale"),
input_types = list(location = "continuous",
scale = "continuous"),
units = units,
element_type = "distribution",
output_var = "x",
output_unit = output_unit,
level = level)
},
#' @description
#' Returns the mode
#'
point_estimate = function() {
self$depends_on$location$point_estimate()
}
),
private = list(
update_output = function(niter) {
sims <- self$simulations %>%
dplyr::mutate(
x = rlogis(niter, location, scale)
)
self$simulations <- sims
},
update_output_level = function(niter0, iter1 = 1, level = 0) {
if (self$level > level) {
niter0 <- 1
}
sims <- self$simulations_multi[[iter1]] %>%
dplyr::mutate(
x = rlogis(niter, location, scale)
)
## Save it
self$simulations_multi[[iter1]] <- sims
## Return the output
invisible(sims[[self$output]])
}
)
)
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