Package {optimizeR}

Title: Unified Framework for Numerical Optimizers
Version: 1.3.0
Description: Provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks.
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 4.0.0)
Imports: checkmate, cli, lbfgsb3c, numDeriv, oeli (≥ 0.7.2), pracma, R6, stats, TestFunctions, ucminf
Suggests: datasets, ggplot2, rmarkdown, testthat
Config/testthat/edition: 3
URL: https://loelschlaeger.de/optimizeR/
BugReports: https://github.com/loelschlaeger/optimizeR/issues
NeedsCompilation: no
Packaged: 2026-05-07 15:40:47 UTC; Lennart Oelschläger
Author: Lennart Oelschläger ORCID iD [aut, cre], Marius Ötting ORCID iD [ctb]
Maintainer: Lennart Oelschläger <oelschlaeger.lennart@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-09 14:41:52 UTC

optimizeR: Unified Framework for Numerical Optimizers

Description

logo

Provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks.

Author(s)

Maintainer: Lennart Oelschläger oelschlaeger.lennart@gmail.com (ORCID)

Other contributors:

See Also

Useful links:

Examples

### Task: compare minimization with 'stats::nlm' and 'pracma::nelder_mead'

# 1. define objective function and initial values
objective <- TestFunctions::TF_ackley
initial <- c(3, 3)

# 2. get overview of optimizers available in dictionary
optimizer_dictionary$keys

# 3. define 'nlm' optimizer
nlm <- Optimizer$new(which = "stats::nlm")

# 4. define the 'pracma::nelder_mead' optimizer
#    (not contained in the dictionary)
nelder_mead <- Optimizer$new(which = "custom")
nelder_mead$definition(
  algorithm = pracma::nelder_mead, # optimization function
  arg_objective = "fn",            # objective function argument name
  arg_initial = "x0",              # argument name for the initial values
  out_value = "fmin",              # element for the optimal function value
  out_parameter = "xmin",          # element for the optimal parameters
  direction = "min"                # optimizer minimizes
)

# 5. compare the minimization results
nlm$minimize(objective, initial)
nelder_mead$minimize(objective, initial)


## ------------------------------------------------
## Method `Optimizer$minimize`
## ------------------------------------------------

Optimizer$new("stats::nlm")$
  minimize(objective = function(x) x^4 + 3*x - 5, initial = 2)

## ------------------------------------------------
## Method `Optimizer$maximize`
## ------------------------------------------------

Optimizer$new("stats::nlm")$
  maximize(objective = function(x) -x^4 + 3*x - 5, initial = 2)

## ------------------------------------------------
## Method `Optimizer$optimize`
## ------------------------------------------------

objective <- function(x) -x^4 + 3*x - 5
optimizer <- Optimizer$new("stats::nlm")
optimizer$optimize(
  objective = objective, initial = 2, direction = "min"
)
optimizer$optimize(
  objective = objective, initial = 2, direction = "max"
)

Specify objective function object

Description

The Objective object specifies the framework for an objective function for numerical optimization.

Value

An Objective object.

Active bindings

objective_name

[character(1)]
The label for the objective function.

fixed_arguments

[character(), read-only]
The name(s) of the fixed argument(s) (if any).

seconds

[numeric(1)]
A time limit in seconds. Computations are interrupted prematurely if seconds is exceeded.

No time limit if seconds = Inf (the default).

Note the limitations documented in setTimeLimit.

hide_warnings

[logical(1)]
Hide warnings when evaluating the objective function?

verbose

[logical(1)]
Print status messages?

npar

[integer(), read-only]
The length of each target argument.

target

[character(), read-only]
The argument name(s) to optimize.

gradient_specified

[logical(1), read-only]
Whether a gradient function has been specified via ⁠$set_gradient()⁠.

hessian_specified

[logical(1), read-only]
Whether a Hessian function has been specified via ⁠$set_hessian()⁠.

Methods

Public methods

Method new()

Creates a new Objective object.

Usage
Objective$new(f, target = NULL, npar, ...)
Arguments
f

[function]
A function to be optimized.

It is expected that f has at least one numeric argument.

Also, the return value of f is expected to have the structure numeric(1), i.e. a single numeric value.

target

[character()]
The argument name(s) to optimize.

All target arguments must be numeric.

If NULL (default), the first function argument is selected.

npar

[integer()]
The length of each target argument, i.e., the length(s) of the numeric vector argument(s) specified by target.

...

Optionally additional function arguments that are fixed during the optimization.

Method set_argument()

Set a function argument that remains fixed during optimization.

Usage
Objective$set_argument(..., .overwrite = TRUE, .verbose = self$verbose)
Arguments
...

Optionally additional function arguments that are fixed during the optimization.

.overwrite

[logical(1)]
Overwrite existing values?

.verbose

[logical(1)]
Print status messages?

Method get_argument()

Get a fixed function argument.

Usage
Objective$get_argument(argument_name, .verbose = self$verbose)
Arguments
argument_name

[character(1)]
A function argument name.

.verbose

[logical(1)]
Print status messages?

Method remove_argument()

Remove a fixed function argument.

Usage
Objective$remove_argument(argument_name, .verbose = self$verbose)
Arguments
argument_name

[character(1)]
A function argument name.

.verbose

[logical(1)]
Print status messages?

Method set_gradient()

Set a gradient function.

Usage
Objective$set_gradient(
  gradient,
  target = self$target,
  npar = self$npar,
  ...,
  .verbose = self$verbose
)
Arguments
gradient

[function]
A function that computes the gradient of the objective function f.

It is expected that gradient has the same call as f, and that gradient returns a numeric vector of length sum(self$npar).

target

[character()]
The argument name(s) to optimize.

All target arguments must be numeric.

If NULL (default), the first function argument is selected.

npar

[integer()]
The length of each target argument, i.e., the length(s) of the numeric vector argument(s) specified by target.

...

Optionally additional function arguments that are fixed during the optimization.

.verbose

[logical(1)]
Print status messages?

Method set_hessian()

Set a Hessian function.

Usage
Objective$set_hessian(
  hessian,
  target = self$target,
  npar = self$npar,
  ...,
  .verbose = self$verbose
)
Arguments
hessian

[function]
A function that computes the Hessian of the objective function f.

It is expected that hessian has the same call as f, and that hessian returns a numeric matrix of dimension sum(self$npar) times sum(self$npar).

target

[character()]
The argument name(s) to optimize.

All target arguments must be numeric.

If NULL (default), the first function argument is selected.

npar

[integer()]
The length of each target argument, i.e., the length(s) of the numeric vector argument(s) specified by target.

...

Optionally additional function arguments that are fixed during the optimization.

.verbose

[logical(1)]
Print status messages?

Method evaluate()

Evaluate the objective function.

Usage
Objective$evaluate(
  .at,
  .negate = FALSE,
  .gradient_as_attribute = FALSE,
  .gradient_attribute_name = "gradient",
  .gradient_numeric = FALSE,
  .hessian_as_attribute = FALSE,
  .hessian_attribute_name = "hessian",
  .hessian_numeric = FALSE,
  ...
)
Arguments
.at

[numeric()]
The values for the target argument(s), written in a single vector.

Must be of length sum(self$npar).

.negate

[logical(1)]
Negate the function return value?

.gradient_as_attribute

[logical(1)]
Add the value of the gradient function as an attribute to the output?

The attribute name is defined via the .gradient_attribute_name argument.

Ignored if ⁠$gradient_specified⁠ and .gradient_numeric are FALSE.

.gradient_attribute_name

[character(1)]
Only relevant if .gradient_as_attribute = TRUE.

In that case, the attribute name for the gradient (if available).

.gradient_numeric

[logical(1)]
Calculate the gradient via the numerical approximation grad?

.hessian_as_attribute

[logical(1)]
Add the value of the Hessian function as an attribute to the output?

The attribute name is defined via the .hessian_attribute_name argument.

Ignored if ⁠$hessian_specified⁠ and hessian_numeric are FALSE.

.hessian_attribute_name

[character(1)]
Only relevant if .hessian_as_attribute = TRUE.

In that case, the attribute name for the Hessian (if available).

.hessian_numeric

[logical(1)]
Calculate the Hessian via the numerical approximation hessian?

...

Optionally additional function arguments that are fixed during the optimization.

Method evaluate_gradient()

Evaluate the gradient function.

Returns a numeric vector of NA_real_ values if the evaluation fails or returns an unexpected result.

Usage
Objective$evaluate_gradient(.at, .negate = FALSE, ...)
Arguments
.at

[numeric()]
The values for the target argument(s), written in a single vector.

Must be of length sum(self$npar).

.negate

[logical(1)]
Negate the function return value?

...

Optionally additional function arguments that are fixed during the optimization.

Method evaluate_gradient_numeric()

Evaluate the numerical gradient grad.

Returns a numeric vector of NA_real_ values if the calculation fails or returns an unexpected result.

Usage
Objective$evaluate_gradient_numeric(.at, .negate = FALSE, ...)
Arguments
.at

[numeric()]
The values for the target argument(s), written in a single vector.

Must be of length sum(self$npar).

.negate

[logical(1)]
Negate the function return value?

...

Optionally additional function arguments that are fixed during the optimization.

Method evaluate_hessian()

Evaluate the Hessian function.

Returns an NA_real_ value or matrix if the evaluation fails or returns an unexpected result.

Usage
Objective$evaluate_hessian(.at, .negate = FALSE, ...)
Arguments
.at

[numeric()]
The values for the target argument(s), written in a single vector.

Must be of length sum(self$npar).

.negate

[logical(1)]
Negate the function return value?

...

Optionally additional function arguments that are fixed during the optimization.

Method evaluate_hessian_numeric()

Evaluate the numerical Hessian hessian.

Returns an NA_real_ matrix if the calculation fails or returns an unexpected result.

Usage
Objective$evaluate_hessian_numeric(.at, .negate = FALSE, ...)
Arguments
.at

[numeric()]
The values for the target argument(s), written in a single vector.

Must be of length sum(self$npar).

.negate

[logical(1)]
Negate the function return value?

...

Optionally additional function arguments that are fixed during the optimization.

Method print()

Print details of the Objective object.

Usage
Objective$print()

Method clone()

The objects of this class are cloneable with this method.

Usage
Objective$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

### define log-likelihood function of Gaussian mixture model
llk <- function(mu, sd, lambda, data) {
  sd <- exp(sd)
  lambda <- plogis(lambda)
  cluster_1 <- lambda * dnorm(data, mu[1], sd[1])
  cluster_2 <- (1 - lambda) * dnorm(data, mu[2], sd[2])
  sum(log(cluster_1 + cluster_2))
}

### optimize over the first three arguments, the 'data' argument is constant
objective <- Objective$new(
  f = llk, target = c("mu", "sd", "lambda"), npar = c(2, 2, 1),
  data = faithful$eruptions
)

### evaluate at 1:5 (1:2 is passed to mu, 3:4 to sd, and 5 to lambda)
objective$evaluate(1:5)

Specify numerical optimizer object

Description

The Optimizer object defines a numerical optimizer based on any optimization algorithm implemented in R. The main advantage of working with an Optimizer object instead of using the optimization function directly lies in the standardized inputs and outputs.

Any R function that fulfills the following four constraints can be defined as an Optimizer object:

  1. It must have an input for a function, the objective function to be optimized.

  2. It must have an input for a numeric vector, the initial values where the optimizer starts.

  3. It must have a ... argument for additional parameters passed on to the objective function.

  4. The output must be a named list, including the optimal function value and the optimal parameter vector.

Active bindings

label

[character(1)]
The label for the optimizer.

algorithm

[function]
The optimization algorithm.

arg_objective

[character(1)]
The argument name for the objective function in algorithm.

arg_initial

[character(1)]
The argument name for the initial values in algorithm.

arg_lower

[character(1) | NA]
Optionally the argument name for the lower parameter bound in algorithm.

Can be NA if not available.

arg_upper

[character(1) | NA]
Optionally the argument name for the upper parameter bound in algorithm.

Can be NA if not available.

arg_gradient

[character(1) | NA]
Optionally the argument name for the gradient function in algorithm.

Can be NA if not available.

arg_hessian

[character(1) | NA]
Optionally the argument name for the Hessian function in algorithm.

Can be NA if not available.

gradient_as_attribute

[logical(1)]
Only relevant if arg_gradient is not NA.

In that case, does algorithm expect that the gradient is an attribute of the objective function output (as for example in nlm)? In that case, arg_gradient defines the attribute name.

hessian_as_attribute

[logical(1)]
Only relevant if arg_hessian is not NA.

In that case, does algorithm expect that the Hessian is an attribute of the objective function output (as for example in nlm)? In that case, arg_hessian defines the attribute name.

out_value

[character(1)]
The element name for the optimal function value in the output list of algorithm.

out_parameter

[character(1)]
The element name for the optimal parameters in the output list of algorithm.

direction

[character(1)]
Either "min" (if the optimizer minimizes) or "max" (if the optimizer maximizes).

arguments

[list()]
Custom arguments for algorithm.

Defaults are used for arguments that are not specified.

seconds

[numeric(1)]
A time limit in seconds.

Optimization is interrupted prematurely if seconds is exceeded.

No time limit if seconds = Inf (the default).

Note the limitations documented in setTimeLimit.

hide_warnings

[logical(1)]
Hide warnings during optimization?

output_ignore

[character()]
Elements to ignore (not include) in the optimization output.

Methods

Public methods

Method new()

Initializes a new Optimizer object.

Usage
Optimizer$new(which, ..., .verbose = TRUE)
Arguments
which

[character(1)]
Either:

  • one of optimizer_dictionary$keys

  • or "custom", in which case $definition() must be used to define the optimizer details.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

.verbose

[logical(1)]
Print status messages?

Returns

A new Optimizer object.

Method definition()

Defines an optimizer.

Usage
Optimizer$definition(
  algorithm,
  arg_objective,
  arg_initial,
  arg_lower = NA,
  arg_upper = NA,
  arg_gradient = NA,
  arg_hessian = NA,
  gradient_as_attribute = FALSE,
  hessian_as_attribute = FALSE,
  out_value,
  out_parameter,
  direction
)
Arguments
algorithm

[function]
The optimization algorithm.

arg_objective

[character(1)]
The argument name for the objective function in algorithm.

arg_initial

[character(1)]
The argument name for the initial values in algorithm.

arg_lower

[character(1) | NA]
Optionally the argument name for the lower parameter bound in algorithm.

Can be NA if not available.

arg_upper

[character(1) | NA]
Optionally the argument name for the upper parameter bound in algorithm.

Can be NA if not available.

arg_gradient

[character(1) | NA]
Optionally the argument name for the gradient function in algorithm.

Can be NA if not available.

arg_hessian

[character(1) | NA]
Optionally the argument name for the Hessian function in algorithm.

Can be NA if not available.

gradient_as_attribute

[logical(1)]
Only relevant if arg_gradient is not NA.

In that case, does algorithm expect that the gradient is an attribute of the objective function output (as for example in nlm)? In that case, arg_gradient defines the attribute name.

hessian_as_attribute

[logical(1)]
Only relevant if arg_hessian is not NA.

In that case, does algorithm expect that the Hessian is an attribute of the objective function output (as for example in nlm)? In that case, arg_hessian defines the attribute name.

out_value

[character(1)]
The element name for the optimal function value in the output list of algorithm.

out_parameter

[character(1)]
The element name for the optimal parameters in the output list of algorithm.

direction

[character(1)]
Either "min" (if the optimizer minimizes) or "max" (if the optimizer maximizes).

Method set_arguments()

Sets optimizer arguments.

Usage
Optimizer$set_arguments(...)
Arguments
...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

The Optimizer object.

Method minimize()

Perform minimization.

Usage
Optimizer$minimize(objective, initial, lower = NA, upper = NA, ...)
Arguments
objective

[function | Objective]
A function to be optimized that

  1. has at least one argument that receives a numeric vector

  2. and returns a single numeric value.

Alternatively, it can also be an Objective object for more flexibility.

initial

[numeric()]
Starting parameter values for the optimization.

lower

[NA | numeric() | numeric(1)]
Lower bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

upper

[NA | numeric() | numeric(1)]
Upper bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

A named list, containing at least these five elements:

value

A numeric, the minimum function value.

parameter

A numeric vector, the parameter vector where the minimum is obtained.

seconds

A numeric, the optimization time in seconds.

initial

A numeric, the initial parameter values.

error

A logical flag: TRUE if an error occurred, otherwise FALSE.

Additional output elements from the optimizer are appended.

If an error occurred, the error message is also appended as element error_message.

If the time limit was exceeded, this counts as an error. In addition, the flag time_out = TRUE is appended.

Examples
Optimizer$new("stats::nlm")$
  minimize(objective = function(x) x^4 + 3*x - 5, initial = 2)

Method maximize()

Perform maximization.

Usage
Optimizer$maximize(objective, initial, lower = NA, upper = NA, ...)
Arguments
objective

[function | Objective]
A function to be optimized that

  1. has at least one argument that receives a numeric vector

  2. and returns a single numeric value.

Alternatively, it can also be an Objective object for more flexibility.

initial

[numeric()]
Starting parameter values for the optimization.

lower

[NA | numeric() | numeric(1)]
Lower bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

upper

[NA | numeric() | numeric(1)]
Upper bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

A named list, containing at least these five elements:

value

A numeric, the maximum function value.

parameter

A numeric vector, the parameter vector where the maximum is obtained.

seconds

A numeric, the optimization time in seconds.

initial

A numeric, the initial parameter values.

error

A logical flag: TRUE if an error occurred, otherwise FALSE.

Additional output elements from the optimizer are appended.

If an error occurred, the error message is also appended as element error_message.

If the time limit was exceeded, this also counts as an error. In addition, the flag time_out = TRUE is appended.

Examples
Optimizer$new("stats::nlm")$
  maximize(objective = function(x) -x^4 + 3*x - 5, initial = 2)

Method optimize()

Perform minimization or maximization.

Usage
Optimizer$optimize(
  objective,
  initial,
  lower = NA,
  upper = NA,
  direction = "min",
  ...
)
Arguments
objective

[function | Objective]
A function to be optimized that

  1. has at least one argument that receives a numeric vector

  2. and returns a single numeric value.

Alternatively, it can also be an Objective object for more flexibility.

initial

[numeric()]
Starting parameter values for the optimization.

lower

[NA | numeric() | numeric(1)]
Lower bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

upper

[NA | numeric() | numeric(1)]
Upper bounds on the parameters.

If a single number, this will be applied to all parameters.

Can be NA to not define any bounds.

direction

[character(1)]
Either "min" for minimization or "max" for maximization.

...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

A named list, containing at least these five elements:

value

A numeric, the optimized function value.

parameter

A numeric vector, the parameter vector where the optimum is obtained.

seconds

A numeric, the optimization time in seconds.

initial

A numeric, the initial parameter values.

error

A logical flag: TRUE if an error occurred, otherwise FALSE.

Additional output elements from the optimizer are appended.

If an error occurred, the error message is also appended as element error_message.

If the time limit was exceeded, this also counts as an error. In addition, the flag time_out = TRUE is appended.

Examples
objective <- function(x) -x^4 + 3*x - 5
optimizer <- Optimizer$new("stats::nlm")
optimizer$optimize(
  objective = objective, initial = 2, direction = "min"
)
optimizer$optimize(
  objective = objective, initial = 2, direction = "max"
)

Method print()

Prints the optimizer label.

Usage
Optimizer$print(...)
Arguments
...

[any]
In Optimizer$new() and $set_arguments(), named arguments to pass to the optimizer algorithm. In $minimize(), $maximize(), and $optimize(), named arguments to pass to the objective function.

Returns

Invisibly the Optimizer object.

Method clone()

The objects of this class are cloneable with this method.

Usage
Optimizer$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

### Task: compare minimization with 'stats::nlm' and 'pracma::nelder_mead'

# 1. define objective function and initial values
objective <- TestFunctions::TF_ackley
initial <- c(3, 3)

# 2. get overview of optimizers available in dictionary
optimizer_dictionary$keys

# 3. define 'nlm' optimizer
nlm <- Optimizer$new(which = "stats::nlm")

# 4. define the 'pracma::nelder_mead' optimizer
#    (not contained in the dictionary)
nelder_mead <- Optimizer$new(which = "custom")
nelder_mead$definition(
  algorithm = pracma::nelder_mead, # optimization function
  arg_objective = "fn",            # objective function argument name
  arg_initial = "x0",              # argument name for the initial values
  out_value = "fmin",              # element for the optimal function value
  out_parameter = "xmin",          # element for the optimal parameters
  direction = "min"                # optimizer minimizes
)

# 5. compare the minimization results
nlm$minimize(objective, initial)
nelder_mead$minimize(objective, initial)


## ------------------------------------------------
## Method `Optimizer$minimize`
## ------------------------------------------------

Optimizer$new("stats::nlm")$
  minimize(objective = function(x) x^4 + 3*x - 5, initial = 2)

## ------------------------------------------------
## Method `Optimizer$maximize`
## ------------------------------------------------

Optimizer$new("stats::nlm")$
  maximize(objective = function(x) -x^4 + 3*x - 5, initial = 2)

## ------------------------------------------------
## Method `Optimizer$optimize`
## ------------------------------------------------

objective <- function(x) -x^4 + 3*x - 5
optimizer <- Optimizer$new("stats::nlm")
optimizer$optimize(
  objective = objective, initial = 2, direction = "min"
)
optimizer$optimize(
  objective = objective, initial = 2, direction = "max"
)

Switch between parameter spaces

Description

The ParameterSpaces object manages two related parameter spaces:

In the Optimization Space, parameters are stored as a numeric vector, the standard format for numerical optimizers.

In the Interpretation Space, parameters are stored as a list and can take different formats (e.g., matrix).

Users can define transformation functions (not necessarily bijective) to switch between these spaces via the $o2i() and $i2o() methods.

Methods

Public methods

Method new()

Initializes a new ParameterSpaces object.

Usage
ParameterSpaces$new(parameter_names, parameter_lengths_in_o_space)
Arguments
parameter_names

[character()]
Unique names for the parameters.

parameter_lengths_in_o_space

[integer()]
The length of each parameter in the optimization space.

Returns

A new ParameterSpaces object.

Method print()

Print an overview of the parameter spaces.

Usage
ParameterSpaces$print(show_transformer = FALSE)
Arguments
show_transformer

[logical(1)]
Show transformer functions in the output?

Method switch()

Switch between Optimization Space and Interpretation Space.

Usage
ParameterSpaces$switch(x, to = NULL)
Arguments
x

[numeric() | list()]
The parameters, either as a ⁠numeric vector⁠ (will be switched to Interpretation Space), or as a list() (will be switched to Optimization Space).

to

[character(1) | NULL]
Explicitly switch to a specific space, either

  • "o": Optimization Space

  • "i": Interpretation Space

If NULL, the function will switch to the other space.

Method o2i()

Define transformation functions when switching from Optimization Space to Interpretation Space.

Usage
ParameterSpaces$o2i(...)
Arguments
...

[function]
One or more transformation functions, named according to the parameters.

Transformers from Optimization Space to Interpretation Space (o2i) must receive a numeric. The default is the identity.

Method i2o()

Define transformation functions when switching from Interpretation Space to Optimization Space.

Usage
ParameterSpaces$i2o(...)
Arguments
...

[function]
One or more transformation functions, named according to the parameters.

Transformers from Interpretation Space to Optimization Space (i2o) must return a numeric. The default is as.vector().

Method clone()

The objects of this class are cloneable with this method.

Usage
ParameterSpaces$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

### Log-likelihood function of two-class Gaussian mixture model with
### parameter vector `theta` that consists of
### - `mu`, mean vector of length 2
### - `sd`, standard deviation vector of length 2, must be positive
### - `lambda`, class probability of length 1, must be between 0 and 1

normal_mixture_llk <- function(theta, data) {
  mu <- theta[1:2]
  sd <- exp(theta[3:4])
  lambda <- plogis(theta[5])
  c1 <- lambda * dnorm(data, mu[1], sd[1])
  c2 <- (1 - lambda) * dnorm(data, mu[2], sd[2])
  sum(log(c1 + c2))
}

### define parameter spaces
### - `mu` needs no transformation
### - `sd` needs to be real in optimization space and positive in
###    interpretation space
### - `lambda` needs to be real and of length `1` in optimization space, and
###    a probability vector of length `2` in interpretation space

normal_mixture_spaces <- ParameterSpaces$
  new(
    parameter_names = c("mu", "sd", "lambda"),
    parameter_lengths_in_o_space = c(2, 2, 1)
  )$
  o2i(
    "mu" = function(x) x,
    "sd" = function(x) exp(x),
    "lambda" = function(x) c(plogis(x), 1 - plogis(x))
  )$
  i2o(
    "mu" = function(x) x,
    "sd" = function(x) log(x),
    "lambda" = function(x) qlogis(x[1])
  )

### switch between parameter spaces

par <- list(              # parameters in interpretation space
  "mu" = c(2, 4),
  "sd" = c(0.5, 1),
  "lambda" = c(0.4, 0.6)
)
(x <- normal_mixture_spaces$switch(par)) # switch to optimization space
normal_mixture_llk(
  theta = x, data = datasets::faithful$eruptions
)
normal_mixture_spaces$switch(x)          # switch back

Dictionary of optimizer functions

Description

A dictionary of currently included numerical optimizer functions in the {optimizeR} package.

Usage

optimizer_dictionary

Format

An R6 object of class Dictionary.

Examples

print(optimizer_dictionary)