Package 'oeli'

Description

These functions check whether the input fulfills the properties of a correlation matrix.

Usage

check_correlation_matrix(x, dim = NULL, tolerance = sqrt(.Machine$double.eps))

assert_correlation_matrix(
  x,
  dim = NULL,
  tolerance = sqrt(.Machine$double.eps),
  .var.name = checkmate::vname(x),
  add = NULL
)

test_correlation_matrix(x, dim = NULL, tolerance = sqrt(.Machine$double.eps))

Arguments

Value

Same as documented in check_matrix.

See Also

Other matrix helpers: check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

M <- matrix(c(1,  0.9,  0.9, 0.9,  1,  -0.9, 0.9,  -0.9,  1), nrow = 3)
check_correlation_matrix(M)
test_correlation_matrix(M)
## Not run: 
assert_correlation_matrix(M)

## End(Not run)

Description

These functions check whether the input fulfills the properties of a covariance matrix.

Usage

check_covariance_matrix(x, dim = NULL, tolerance = sqrt(.Machine$double.eps))

assert_covariance_matrix(
  x,
  dim = NULL,
  tolerance = sqrt(.Machine$double.eps),
  .var.name = checkmate::vname(x),
  add = NULL
)

test_covariance_matrix(x, dim = NULL, tolerance = sqrt(.Machine$double.eps))

Arguments

Value

Same as documented in check_matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

M <- matrix(c(1, 2, 3, 2, 1, 2, 3, 2, 1), nrow = 3)
check_covariance_matrix(M)
test_covariance_matrix(M)
## Not run: 
assert_covariance_matrix(M)

## End(Not run)

Description

These functions check whether the input is a list that contains list elements.

Usage

check_list_of_lists(x, len = NULL)

assert_list_of_lists(
  x,
  len = NULL,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_list_of_lists(x, len = NULL)

Arguments

Value

Same as documented in check_list.

See Also

Other list helpers: merge_lists()

Examples

L <- list(list(1), list(2), 3)
check_list_of_lists(L)
test_list_of_lists(L)
## Not run: 
assert_list_of_lists(L)

## End(Not run)

Description

These functions check whether a value was specified as an argument to a function.

Usage

check_missing(x)

assert_missing(x)

test_missing(x)

Arguments

Value

Depending on the function prefix:

• If the check is successful, assert_missing() returns x invisibly, whereas check_missing() and test_missing() return TRUE.

• If the check is not successful, assert_missing() throws an error message, test_missing() returns FALSE, and check_missing() returns a string with the error message.

See Also

Other package helpers: Dictionary, Storage, find_namespace_calls(), find_pkg_functions(), identical_structure(), input_check_response(), match_arg(), package_logo(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

f <- function(x) {
  check_missing(x)
}
f()

g <- function(x) {
  test_missing(x)
}
g()

h <- function(x) {
  assert_missing(x)
}
## Not run: 
h()

## End(Not run)

Description

These functions check whether the input is a numeric vector.

Usage

check_numeric_vector(
  x,
  lower = -Inf,
  upper = Inf,
  finite = FALSE,
  any.missing = TRUE,
  all.missing = TRUE,
  len = NULL,
  min.len = NULL,
  max.len = NULL,
  unique = FALSE,
  sorted = FALSE,
  names = NULL,
  typed.missing = FALSE,
  null.ok = FALSE
)

assert_numeric_vector(
  x,
  lower = -Inf,
  upper = Inf,
  finite = FALSE,
  any.missing = TRUE,
  all.missing = TRUE,
  len = NULL,
  min.len = NULL,
  max.len = NULL,
  unique = FALSE,
  sorted = FALSE,
  names = NULL,
  typed.missing = FALSE,
  null.ok = FALSE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_numeric_vector(
  x,
  lower = -Inf,
  upper = Inf,
  finite = FALSE,
  any.missing = TRUE,
  all.missing = TRUE,
  len = NULL,
  min.len = NULL,
  max.len = NULL,
  unique = FALSE,
  sorted = FALSE,
  names = NULL,
  typed.missing = FALSE,
  null.ok = FALSE
)

Arguments

Value

Same as documented in check_numeric.

See Also

Other vector helpers: check_probability_vector(), chunk_vector(), equidistant_vectors(), insert_vector_entry(), map_indices(), match_numerics(), permutations(), split_vector_at(), subsets(), vector_occurrence()

Examples

x <- c(1, 2, "3")
check_numeric_vector(x)
test_numeric_vector(x)
## Not run: 
assert_numeric_vector(x)

## End(Not run)

Description

These functions check whether the input is a one-hot matrix, i.e., a numeric matrix where each row contains exactly one entry equal to 1 and all other entries equal to 0.

Usage

check_one_hot_matrix(
  x,
  nrows = NULL,
  ncols = NULL,
  tolerance = sqrt(.Machine$double.eps)
)

assert_one_hot_matrix(
  x,
  nrows = NULL,
  ncols = NULL,
  tolerance = sqrt(.Machine$double.eps),
  .var.name = checkmate::vname(x),
  add = NULL
)

test_one_hot_matrix(
  x,
  nrows = NULL,
  ncols = NULL,
  tolerance = sqrt(.Machine$double.eps)
)

Arguments

Value

Same as documented in check_matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

x <- matrix(c(
  1, 0, 0, 0,
  0, 1, 0, 0,
  0, 0, 0, 0
), nrow = 3, byrow = TRUE)

check_one_hot_matrix(x)
test_one_hot_matrix(x)
## Not run: 
assert_one_hot_matrix(x)

## End(Not run)

Description

These functions check whether the input fulfills the properties of a probability matrix.

Usage

check_probability_vector(x, len = NULL, tolerance = sqrt(.Machine$double.eps))

assert_probability_vector(
  x,
  len = NULL,
  tolerance = sqrt(.Machine$double.eps),
  .var.name = checkmate::vname(x),
  add = NULL
)

test_probability_vector(x, len = NULL, tolerance = sqrt(.Machine$double.eps))

Arguments

Value

Same as documented in check_numeric.

See Also

Other vector helpers: check_numeric_vector(), chunk_vector(), equidistant_vectors(), insert_vector_entry(), map_indices(), match_numerics(), permutations(), split_vector_at(), subsets(), vector_occurrence()

Examples

p <- c(0.2, 0.3, 0.6)
check_probability_vector(p)
test_probability_vector(p)
## Not run: 
assert_probability_vector(p)

## End(Not run)

Description

These functions check whether the input is a transition probability matrix.

Usage

check_transition_probability_matrix(
  x,
  dim = NULL,
  tolerance = sqrt(.Machine$double.eps)
)

assert_transition_probability_matrix(
  x,
  dim = NULL,
  tolerance = sqrt(.Machine$double.eps),
  .var.name = checkmate::vname(x),
  add = NULL
)

test_transition_probability_matrix(
  x,
  dim = NULL,
  tolerance = sqrt(.Machine$double.eps)
)

Arguments

Value

Same as documented in check_matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

T <- matrix(c(0.8,  0.2,  0.1, 0.1,  0.7,  0.4, 0.1,  0.1,  0.6), nrow = 3)
check_transition_probability_matrix(T)
test_transition_probability_matrix(T)
## Not run: 
assert_transition_probability_matrix(T)

## End(Not run)

Description

This function either

• splits a vector into n chunks of equal size (type = 1),

• splits a vector into chunks of size n (type = 2).

Usage

chunk_vector(x, n, type = 1, strict = FALSE)

Arguments

Value

A list.

See Also

Other vector helpers: check_numeric_vector(), check_probability_vector(), equidistant_vectors(), insert_vector_entry(), map_indices(), match_numerics(), permutations(), split_vector_at(), subsets(), vector_occurrence()

Examples

x <- 1:12
chunk_vector(x, n = 3, type = 1)
chunk_vector(x, n = 3, type = 2)
try(chunk_vector(x, n = 5, strict = TRUE))

Description

This function simulates regressor values from various marginal distributions with custom correlations.

Usage

correlated_regressors(
  labels,
  n = 100,
  marginals = list(),
  correlation = diag(length(labels)),
  verbose = FALSE
)

Arguments

Value

A data.frame with n rows and length(labels) columns.

References

This function heavily depends on the {SimMultiCorrData} package.

See Also

Other simulation helpers: Simulator, ddirichlet_cpp(), dmixnorm_cpp(), dmvnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

labels <- c("P", "C", "N1", "N2", "U")
n <- 100
marginals <- list(
  "P" = list(type = "poisson", lambda = 2),
  "C" = list(type = "categorical", p = c(0.3, 0.2, 0.5)),
  "N1" = list(type = "normal", mean = -1, sd = 2),
  "U" = list(type = "uniform", min = -2, max = -1)
)
correlation <- matrix(
  c(1, -0.3, -0.1, 0, 0.5,
    -0.3, 1, 0.3, -0.5, -0.7,
    -0.1, 0.3, 1, -0.3, -0.3,
    0, -0.5, -0.3, 1, 0.1,
    0.5, -0.7, -0.3, 0.1, 1),
  nrow = 5, ncol = 5
)
data <- correlated_regressors(
  labels = labels, n = n, marginals = marginals, correlation = correlation
)
head(data)

Description

These functions compute the Cholesky root elements of a covariance matrix and, conversely, build a covariance matrix from its Cholesky root elements.

Usage

cov_to_chol(cov, unique = TRUE)

chol_to_cov(chol)

unique_chol(chol)

Arguments

Value

For cov_to_chol a numeric vector of Cholesky root elements.

For chol_to_cov a covariance matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

cov <- sample_covariance_matrix(4)
chol <- cov_to_chol(cov)
all.equal(cov, chol_to_cov(chol))

Description

The function ddirichlet() computes the density of a Dirichlet distribution.

The function rdirichlet() samples from a Dirichlet distribution.

The functions with suffix ⁠_cpp⁠ perform no input checks, hence are faster.

Usage

ddirichlet_cpp(x, concentration, log = FALSE)

rdirichlet_cpp(concentration)

ddirichlet(x, concentration, log = FALSE)

rdirichlet(n = 1, concentration)

Arguments

Value

For ddirichlet(): The density value.

For rdirichlet(): If n = 1 a vector of length p, else a matrix of dimension n times p with samples as rows.

See Also

Other simulation helpers: Simulator, correlated_regressors(), dmixnorm_cpp(), dmvnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

x <- c(0.5, 0.3, 0.2)
concentration <- 1:3

# compute density
ddirichlet(x = x, concentration = concentration)
ddirichlet(x = x, concentration = concentration, log = TRUE)

# sample
rdirichlet(concentration = 1:3)
rdirichlet(n = 4, concentration = 1:2)

Description

This function deletes columns of a data.frame by name.

Usage

delete_columns_data.frame(df, column_names)

Arguments

Value

The input df without the columns defined by column_names.

See Also

Other data.frame helpers: group_data.frame(), occurrence_info(), round_data.frame()

Examples

df <- data.frame("label" = c("A", "B"), "number" = 1:10)
delete_columns_data.frame(df = df, column_names = "label")
delete_columns_data.frame(df = df, column_names = "number")
delete_columns_data.frame(df = df, column_names = c("label", "number"))

Description

Provides a simple key-value interface based on R6.

Active bindings

Methods

Public methods

• Dictionary$new()

• Dictionary$add()

• Dictionary$get()

• Dictionary$remove()

• Dictionary$print()

Method new()

Initializing a new Dictionary object.

Usage

Dictionary$new(
  key_name,
  alias_name = NULL,
  value_names = character(),
  value_assert = alist(),
  allow_overwrite = TRUE,
  keys_reserved = character(),
  alias_choices = NULL,
  dictionary_name = NULL
)

Arguments

Method add()

Adding an element to the dictionary.

Usage

Dictionary$add(...)

Arguments

Method get()

Getting elements from the dictionary.

Usage

Dictionary$get(key, value = NULL)

Arguments

Method remove()

Removing elements from the dictionary (and associated alias, if any).

Usage

Dictionary$remove(key)

Arguments

Method print()

Printing details of the dictionary.

Usage

Dictionary$print()

See Also

Other package helpers: Storage, check_missing(), find_namespace_calls(), find_pkg_functions(), identical_structure(), input_check_response(), match_arg(), package_logo(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

# Managing variable metadata for a dataset

meta_dict <- Dictionary$new(
  key_name = "var_name",
  alias_name = "category",
  value_names = c("label", "type"),
  value_assert = alist(
    label = checkmate::assert_string(),
    type = checkmate::assert_choice(
      choices = c("numeric", "factor", "character")
    )
  ),
  allow_overwrite = FALSE,
  keys_reserved = c("id"),
  alias_choices = c("demographics", "outcome", "other"),
  dictionary_name = "Variable Metadata"
)

# Add entries to the dictionary
meta_dict$add(
  var_name = "age",
  label = "Age of respondent",
  type = "numeric",
  category = "demographics"
)

meta_dict$add(
  var_name = "gender",
  label = "Gender identity",
  type = "factor",
  category = "demographics"
)

meta_dict$add(
  var_name = "income",
  label = "Annual income in USD",
  type = "numeric",
  category = c("demographics", "outcome")
)

# Print dictionary
meta_dict$print()

# Retrieve full metadata for a variable
meta_dict$get("income")

# Retrieve a specific piece of metadata
meta_dict$get("income", value = "label")

# Show variables by category
meta_dict$alias

Description

These functions difference and un-difference random vectors and covariance matrices.

Usage

diff_cov(cov, ref = 1)

undiff_cov(cov_diff, ref = 1)

delta(ref = 1, dim)

M(ranking = seq_len(dim), dim)

Arguments

Details

Assume $x \sim N(0, \Sigma)$ is a multivariate normally distributed random vector of dimension $n$. We may want to consider the differenced vector

$\tilde x = (x_1 - x_k, x_2 - x_k, \dots, x_n - x_k)',$

excluding the $k$th element (hence, $\tilde x$ is of dimension $(n - 1) \times 1$). Formally, $\tilde x = \Delta_k x$, where $\Delta_k$ is a difference operator that depends on the reference row $k$. More precise, $\Delta_k$ is the identity matrix of dimension $n$ without row $k$ and with $-1$s in column $k$. The difference operator $\Delta_k$ can be computed via delta(ref = k, dim = n).

Then, $\tilde x \sim N(0, \tilde \Sigma)$, where

$\tilde{\Sigma} = \Delta_k \Sigma \Delta_k'$

is the differenced covariance matrix with respect to row $k = 1,\dots,n$. The differenced covariance matrix $\tilde \Sigma$ can be computed via diff_delta(Sigma, ref = k).

Since $\Delta_k$ is a non-bijective mapping, $\Sigma$ cannot be uniquely restored from $\tilde \Sigma$. However, it is possible to compute a non-unique solution $\Sigma_0$, such that $\Delta_k \Sigma_0 \Delta_k = \tilde \Sigma$. For such a non-unique solution, we add a column and a row of zeros at column and row number $k$ to $\tilde{\Sigma}$, respectively. An "un-differenced" covariance matrix $\Sigma_0$ can be computed via undiff_delta(Sigma_diff, ref = k).

As a alternative to $\Delta_k$, the function M() returns a matrix for taking differences such that the resulting vector is negative.

Value

A (differenced or un-differenced) covariance matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

n <- 4
Sigma <- sample_covariance_matrix(dim = n)
k <- 2
x <- c(1, 3, 2, 4)

# build difference operator
delta_k <- delta(ref = k, dim = n)

# difference vector
delta_k %*% x

# difference Sigma
(Sigma_diff <- diff_cov(Sigma, ref = k))

# un-difference Sigma
(Sigma_0 <- undiff_cov(Sigma_diff, ref = k))

# difference again
Sigma_diff_2 <- diff_cov(Sigma_0, ref = k)
all.equal(Sigma_diff, Sigma_diff_2)

# difference such that the resulting vector is negative
M(ranking = order(x, decreasing = TRUE), dim = n) %*% x

Description

The function dmixnorm() computes the density of a mixture of multivariate normal distribution.

The function pmixnorm() computes the cumulative distribution function of a mixture of multivariate normal distribution.

The function rmixnorm() samples from a mixture of multivariate normal distribution.

The functions with suffix ⁠_cpp⁠ perform no input checks, hence are faster.

The univariate normal mixture is available as the special case p = 1.

Usage

dmixnorm_cpp(x, mean, Sigma, proportions)

pmixnorm_cpp(x, mean, Sigma, proportions, abseps = 0.001)

rmixnorm_cpp(mean, Sigma, proportions)

dmixnorm(x, mean, Sigma, proportions)

pmixnorm(x, mean, Sigma, proportions, abseps = 0.001)

rmixnorm(n = 1, mean, Sigma, proportions)

Arguments

Details

pmixnorm() is based on mvtnorm::pmvnorm with the randomized Quasi-Monte-Carlo procedure by Genz and Bretz. The argument abseps controls the accuracy of the Gaussian integral approximation.

Value

For dmixnorm(): The density value.

For pmixnorm(): The value of the distribution function.

For rmixnorm(): If n = 1 a vector of length p (note that it is a column vector for rmixnorm_cpp()), else a matrix of dimension n times p with samples as rows.

See Also

Other simulation helpers: Simulator, correlated_regressors(), ddirichlet_cpp(), dmvnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

x <- c(0, 0)
mean <- matrix(c(-1, -1, 0, 0), ncol = 2)
Sigma <- matrix(c(diag(2), diag(2)), ncol = 2)
proportions <- c(0.7, 0.3)

# compute density
dmixnorm(x = x, mean = mean, Sigma = Sigma, proportions = proportions)

# compute CDF
pmixnorm(x = x, mean = mean, Sigma = Sigma, proportions = proportions)

# sample
rmixnorm(n = 3, mean = mean, Sigma = Sigma, proportions = proportions)

Description

The function dmvnorm() computes the density of a multivariate normal distribution.

The function pmvnorm() computes the cumulative distribution function of a multivariate normal distribution.

The function rmvnorm() samples from a multivariate normal distribution.

The functions with suffix ⁠_cpp⁠ perform no input checks, hence are faster.

The univariate normal distribution is available as the special case p = 1.

Usage

dmvnorm_cpp(x, mean, Sigma, log = FALSE)

pmvnorm_cpp(x, mean, Sigma, abseps = 0.001)

rmvnorm_cpp(mean, Sigma, log = FALSE)

dmvnorm(x, mean, Sigma, log = FALSE)

pmvnorm(x, mean, Sigma, abseps = 0.001)

rmvnorm(n = 1, mean, Sigma, log = FALSE)

Arguments

Details

pmvnorm() just calls mvtnorm::pmvnorm with the randomized Quasi-Monte-Carlo procedure by Genz and Bretz. The argument abseps controls the accuracy of the Gaussian integral approximation.

Value

For dmvnorm(): The density value.

For pmvnorm(): The value of the distribution function.

For rmvnorm(): If n = 1 a vector of length p (note that it is a column vector for rmvnorm_cpp()), else a matrix of dimension n times p with samples as rows.

See Also

Other simulation helpers: Simulator, correlated_regressors(), ddirichlet_cpp(), dmixnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

x <- c(0, 0)
mean <- c(0, 0)
Sigma <- diag(2)

# compute density
dmvnorm(x = x, mean = mean, Sigma = Sigma)
dmvnorm(x = x, mean = mean, Sigma = Sigma, log = TRUE)

# compute CDF
pmvnorm(x = x, mean = mean, Sigma = Sigma)

# sample
rmvnorm(n = 3, mean = mean, Sigma = Sigma)
rmvnorm(mean = mean, Sigma = Sigma, log = TRUE)

Description

This function measures the computation time of a call.

Usage

do.call_timed(what, args, units = "secs")

Arguments

Details

This function is a wrapper for do.call.

Value

A list of the two elements "result" (the results of the do.call call) and "time" (the computation time).

See Also

Other function helpers: function_arguments(), function_body(), function_defaults(), quiet(), timed(), try_silent(), variable_name()

Examples

## Not run: 
what <- function(s) {
  Sys.sleep(s)
  return(s)
}
args <- list(s = 1)
do.call_timed(what = what, args = args)

## End(Not run)

Description

The function dtnorm() computes the density of a truncated normal distribution.

The function rtnorm() samples from a truncated normal distribution.

The function dttnorm() and rttnorm() compute the density and sample from a two-sided truncated normal distribution, respectively.

The functions with suffix ⁠_cpp⁠ perform no input checks, hence are faster.

Usage

dtnorm_cpp(x, mean, sd, point, above, log = FALSE)

dttnorm_cpp(x, mean, sd, lower, upper, log = FALSE)

rtnorm_cpp(mean, sd, point, above, log = FALSE)

rttnorm_cpp(mean, sd, lower, upper, log = FALSE)

dtnorm(x, mean, sd, point, above, log = FALSE)

dttnorm(x, mean, sd, lower, upper, log = FALSE)

rtnorm(mean, sd, point, above, log = FALSE)

rttnorm(mean, sd, lower, upper, log = FALSE)

Arguments

Value

For dtnorm() and dttnorm(): The density value.

For rtnorm() and rttnorm(): The random draw

See Also

Other simulation helpers: Simulator, correlated_regressors(), ddirichlet_cpp(), dmixnorm_cpp(), dmvnorm_cpp(), dwishart_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

x <- c(0, 0)
mean <- c(0, 0)
Sigma <- diag(2)

# compute density
dmvnorm(x = x, mean = mean, Sigma = Sigma)
dmvnorm(x = x, mean = mean, Sigma = Sigma, log = TRUE)

# sample
rmvnorm(n = 3, mean = mean, Sigma = Sigma)
rmvnorm(mean = mean, Sigma = Sigma, log = TRUE)

Description

The function dwishart() computes the density of a Wishart distribution.

The function rwishart() samples from a Wishart distribution.

The functions with suffix ⁠_cpp⁠ perform no input checks, hence are faster.

Usage

dwishart_cpp(x, df, scale, log = FALSE, inv = FALSE)

rwishart_cpp(df, scale, inv = FALSE)

dwishart(x, df, scale, log = FALSE, inv = FALSE)

rwishart(df, scale, inv = FALSE)

Arguments

Value

For dwishart(): The density value.

For rwishart(): A matrix, the random draw.

See Also

Other simulation helpers: Simulator, correlated_regressors(), ddirichlet_cpp(), dmixnorm_cpp(), dmvnorm_cpp(), dtnorm_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

x <- diag(2)
df <- 6
scale <- matrix(c(1, -0.3, -0.3, 0.8), ncol = 2)

# compute density
dwishart(x = x, df = df, scale = scale)
dwishart(x = x, df = df, scale = scale, log = TRUE)
dwishart(x = x, df = df, scale = scale, inv = TRUE)

# sample
rwishart(df = df, scale = scale)
rwishart(df = df, scale = scale, inv = TRUE)

# expectation of Wishart is df * scale
n <- 100
replicate(n, rwishart(df = df, scale = scale), simplify = FALSE) |>
  Reduce(f = "+") / n
df * scale

# expectation of inverse Wishart is scale / (df - p - 1)
n <- 100
replicate(n, rwishart(df = df, scale = scale, TRUE), simplify = FALSE) |>
  Reduce(f = "+") / n
scale / (df - 2 - 1)

Description

This function constructs the coordinates of vertices of a regular simplex in $\mathbb{R}^{\code{dim}}$ and returns the first n of them,

• scaled so that the pairwise Euclidean distance between any two vertices equals dist,

• and centered so their centroid is at center.

Usage

equidistant_vectors(dim, n = dim + 1, dist = 1, center = rep(0, dim))

Arguments

Value

A matrix, where each column is a vertex of the simplex.

See Also

Other vector helpers: check_numeric_vector(), check_probability_vector(), chunk_vector(), insert_vector_entry(), map_indices(), match_numerics(), permutations(), split_vector_at(), subsets(), vector_occurrence()

Examples

dim <- n <- 3
(dist <- runif(1))
(center <- rnorm(dim))
(V <- equidistant_vectors(dim = dim, n = n, dist = dist, center = center))
rowMeans(V)
dist(t(V))

Description

This function searches for namespace calls in .R files, i.e., code lines of the format ⁠<package name>::<function name>⁠.

Usage

find_namespace_calls(path = "R", triple_colon = FALSE, as_list = FALSE)

Arguments

Value

A data.frame. If as_list = TRUE, a list.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_pkg_functions(), identical_structure(), input_check_response(), match_arg(), package_logo(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

## Not run: 
find_namespace_calls()
find_namespace_calls(as_list = TRUE)

## End(Not run)

Description

This function lists R functions found in an R package. It can inspect the loaded namespace for exported and non-exported functions and, if a package source path is available, scan .R files in ⁠R/⁠.

Usage

find_pkg_functions(pkg, include_namespace = TRUE, include_source = TRUE)

Arguments

Details

include_namespace = TRUE inspects the package namespace with asNamespace(). This works for installed packages and returns the R functions that are actually available after the package is loaded, including internal functions.

include_source = TRUE scans the package source files under ⁠R/⁠. This is useful when pkg points to a package source directory, for example while developing a package before it is installed. If both options are TRUE, the result is combined and duplicate function names are removed.

The title column is read from Rd documentation aliases. It is NA when no matching Rd documentation entry is available for a function.

Value

A tibble with one row per found R function and columns name, title, exported, and signature.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_namespace_calls(), identical_structure(), input_check_response(), match_arg(), package_logo(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

## Not run: 
find_pkg_functions("R6")
find_pkg_functions(".")

## End(Not run)

Description

This function returns the names of function arguments.

Usage

function_arguments(f, with_default = TRUE, with_ellipsis = TRUE)

Arguments

Value

A character vector.

See Also

Other function helpers: do.call_timed(), function_body(), function_defaults(), quiet(), timed(), try_silent(), variable_name()

Examples

f <- function(a, b = 1, c = "", ...) { }
function_arguments(f)
function_arguments(f, with_default = FALSE)
function_arguments(f, with_ellipsis = FALSE)

Description

This function extracts the body of a function as a single character.

Usage

function_body(fun, braces = FALSE, nchar = getOption("width") - 4)

Arguments

Value

A character, the body of f.

See Also

Other function helpers: do.call_timed(), function_arguments(), function_defaults(), quiet(), timed(), try_silent(), variable_name()

Examples

fun <- mean.default
function_body(fun)
function_body(fun, braces = TRUE)
function_body(fun, nchar = 30)

Description

This function returns the default function arguments (if any).

Usage

function_defaults(f, exclude = NULL)

Arguments

Value

A named list.

See Also

Other function helpers: do.call_timed(), function_arguments(), function_body(), quiet(), timed(), try_silent(), variable_name()

Examples

f <- function(a, b = 1, c = "", ...) { }
function_defaults(f)
function_defaults(f, exclude = "b")

Description

Computes the total variation (TV) between two multivariate Gaussian distributions $f_1,f_2$:

$\mathrm{TV}(f_1, f_2) = \tfrac{1}{2} \int_{\mathbb{R}^p} \lvert f_1(x) - f_2(x) \rvert \, dx.$

The value ranges from 0 (identical distributions) to 1 (no overlap).

Usage

gaussian_tv(
  mean1,
  mean2,
  Sigma1,
  Sigma2,
  method = c("auto", "mc", "cubature"),
  n = 10000,
  tol_equal = 1e-06,
  eps = 1e-06
)

Arguments

Value

The total variation in [0, 1].

See Also

Other simulation helpers: Simulator, correlated_regressors(), ddirichlet_cpp(), dmixnorm_cpp(), dmvnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), simulate_markov_chain()

Examples

### univariate case
mean1 <- 0
mean2 <- 1
Sigma1 <- Sigma2 <- matrix(1)
gaussian_tv(mean1, mean2, Sigma1, Sigma2)

### bivariate case
mean1 <- c(0, 0)
mean2 <- c(1, 1)
Sigma1 <- matrix(c(1, 0.2, 0.2, 1), ncol = 2)
Sigma2 <- matrix(c(1.5, -0.3, -0.3, 1), ncol = 2)
gaussian_tv(mean1, mean2, Sigma1, Sigma2, method = "mc", n = 1e3)

Description

This function groups a data.frame according to values of one column.

Usage

group_data.frame(df, by, keep_by = TRUE)

Arguments

Value

A list of data.frames, subsets of df.

See Also

Other data.frame helpers: delete_columns_data.frame(), occurrence_info(), round_data.frame()

Examples

df <- data.frame("label" = c("A", "B"), "number" = 1:10)
group_data.frame(df = df, by = "label")
group_data.frame(df = df, by = "label", keep_by = FALSE)

Description

The Hermannslauf is an annual long-distance road and trail race in Germany that runs from Detmold to Bielefeld through the Teutoburg Forest. It is one of the best-known running events in the region and is especially noted for its demanding, hilly course of about 31 kilometers. This dataset contains historical information on editions of the race, including the date, temperature, and winning times for men and women.

From 1972 to 1976, the course followed the actual Hermannsweg for 30.4 kilometers, and in 1977 the first 13 kilometers were moved to a parallel route. Since 2005, the total course length has been 31.1 kilometers; until 2004, it was 30.6 kilometers. In 2020, the Hermannslauf was canceled due to the pandemic, and for the same reason, in 2021 it was exceptionally moved from April to October.

Usage

hermann

Format

A tibble with 54 rows and 8 columns:

edition

the edition of the Hermannslauf

year

the year

date

the date

temp

the temperature on that day at 12:00 noon; see details

winner_men

the men's winner

seconds_men

the men's winner's total time in seconds

winner_women

the women's winner

seconds_women

the women's winner's total time in seconds

Details

Temperature in degrees Celsius, measured at 12:00 noon, obtained from different sources:

• from 1973 to 1992 and from 1998 to 2013 from https://de.weatherspark.com/, Gütersloh Airport (approx. 30 km from start and finish)

• from 1993 to 1997 from https://de.weatherspark.com/, Wunstorf Air Base (approx. 100 km from start and finish)

• from 2014 to 2017 from https://de.weatherspark.com/, Hannover-Langenhagen Airport (approx. 90 km from start and finish)

• from 2018 onward from https://meteostat.net/de/station/D7106, Airfield Bielefeld-Windelsbleiche (approx. 20 km from start and finish)

Source

https://de.wikipedia.org/wiki/Hermannslauf

Description

This function determines whether two objects have the same structure,

• which includes the mode, class and dimension

• but does not include concrete values or attributes.

Usage

identical_structure(x, y)

Arguments

Value

Either TRUE if x and y have the same structure, and FALSE, else.

References

Inspired by https://stackoverflow.com/a/45548885/15157768.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_namespace_calls(), find_pkg_functions(), input_check_response(), match_arg(), package_logo(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

identical_structure(integer(1), 1L)
identical_structure(diag(2), matrix(rnorm(4), 2, 2))
identical_structure(diag(2), data.frame(diag(2)))

Description

This function provides a standardized response to input checks, ensuring consistency.

Usage

input_check_response(
  check,
  var_name = NULL,
  error = TRUE,
  prefix = "Input {.var {var_name}} is bad:"
)

Arguments

Value

TRUE if check is TRUE (or any element in check is TRUE, if check is a list) . Else, depending on error:

• If error is TRUE, throws an error.

• If error is FALSE, returns FALSE.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_namespace_calls(), find_pkg_functions(), identical_structure(), match_arg(), package_logo(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

x <- "1"
y <- 1

### check is successful
input_check_response(
  check = checkmate::check_character(x),
  var_name = "x",
  error = TRUE
)

### alternative checks
input_check_response(
  check = list(
    checkmate::check_character(x),
    checkmate::check_character(y)
  ),
  var_name = "x",
  error = TRUE
)

### standardized check response
## Not run: 
input_check_response(
  check = checkmate::check_character(y),
  var_name = "y",
  error = TRUE
)

input_check_response(
  check = list(
    checkmate::check_flag(x),
    checkmate::check_character(y)
  ),
  var_name = "y",
  error = TRUE
)

## End(Not run)

Description

This function inserts a column into a matrix.

Usage

insert_matrix_column(A, x, p)

Arguments

Value

A matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

A <- diag(3)
x <- 1:3
insert_matrix_column(A, x, 0)
insert_matrix_column(A, x, 1)
insert_matrix_column(A, x, 2)
insert_matrix_column(A, x, 3)

### also single value
x <- 2
insert_matrix_column(A, x, 0)

### also multiple positions
insert_matrix_column(A, x, 0:3)

### also trivial case
insert_matrix_column(matrix(nrow = 0, ncol = 0), integer(), integer())

Description

This function inserts a value into a vector.

Usage

insert_vector_entry(v, x, p)

Arguments

Value

A vector.

See Also

Other vector helpers: check_numeric_vector(), check_probability_vector(), chunk_vector(), equidistant_vectors(), map_indices(), match_numerics(), permutations(), split_vector_at(), subsets(), vector_occurrence()

Examples

v <- 1:3
x <- 0
insert_vector_entry(v, x, 0)
insert_vector_entry(v, x, 1)
insert_vector_entry(v, x, 2)
insert_vector_entry(v, x, 3)

### also multiple positions
insert_vector_entry(v, x, 0:3)

### also trivial case
insert_vector_entry(integer(), integer(), integer())

Description

This function maps indices from an input vector to corresponding sequences of grouped indices. Each element from the input specifies a group to be mapped from the sequence, determined by the grouping size n.

Usage

map_indices(indices, n)

Arguments

Details

This function is useful when working with indices arranged in fixed-size groups, where each group can be referenced by a single index. For example, if indices are structured in chunks of 3, calling this function with n = 3 will map the corresponding groups of 3 consecutive indices for the given input indices, see the examples.

Value

An integer vector, containing the mapped indices according to the specified group size.

See Also

Other vector helpers: check_numeric_vector(), check_probability_vector(), chunk_vector(), equidistant_vectors(), insert_vector_entry(), match_numerics(), permutations(), split_vector_at(), subsets(), vector_occurrence()

Examples

# Example: Map indices based on groups of 3
map_indices(c(1, 3, 5), 3)

Description

This function matches function arguments and is a modified version of match.arg.

Usage

match_arg(arg, choices, several.ok = FALSE, none.ok = FALSE)

Arguments

Value

The un-abbreviated version of the exact or unique partial match if there is one. Otherwise, an error is signaled if several.ok is FALSE or none.ok is FALSE. When several.ok is TRUE and (at least) one element of arg has a match, all un-abbreviated versions of matches are returned. When none.ok is TRUE and arg has zero elements, character(0) is returned.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_namespace_calls(), find_pkg_functions(), identical_structure(), input_check_response(), package_logo(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Description

This function matches the indices of two numeric vectors as good as possible (that means with the smallest possible sum of deviations).

Usage

match_numerics(x, y)

Arguments

Value

An integer vector of length length(x) with the positions of y in x.

See Also

Other vector helpers: check_numeric_vector(), check_probability_vector(), chunk_vector(), equidistant_vectors(), insert_vector_entry(), map_indices(), permutations(), split_vector_at(), subsets(), vector_occurrence()

Examples

x <- c(-1, 0, 1)
y <- c(0.1, 1.5, -1.2)
match_numerics(x, y)

Description

This function returns the indices of the diagonal elements of a quadratic matrix.

Usage

matrix_diagonal_indices(n, triangular = NULL)

Arguments

Value

An integer vector.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

# indices of diagonal elements
n <- 3
matrix(1:n^2, n, n)
matrix_diagonal_indices(n)

# indices of diagonal elements of lower-triangular matrix
L <- matrix(0, n, n)
L[lower.tri(L, diag=TRUE)] <- 1:((n * (n + 1)) / 2)
L
matrix_diagonal_indices(n, triangular = "lower")

# indices of diagonal elements of upper-triangular matrix
U <- matrix(0, n, n)
U[upper.tri(U, diag=TRUE)] <- 1:((n * (n + 1)) / 2)
U
matrix_diagonal_indices(n, triangular = "upper")

Description

This function returns matrix indices as character.

Usage

matrix_indices(x, prefix = "", exclude_diagonal = FALSE)

Arguments

Value

A character vector.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

M <- diag(3)
matrix_indices(M)
matrix_indices(M, "M_")
matrix_indices(M, "M_", TRUE)

Description

This function merges lists based on their element names. Elements are only included in the final output list, if no former list has contributed an element with the same name.

Usage

merge_lists(...)

Arguments

Value

A list.

See Also

Other list helpers: check_list_of_lists()

Examples

merge_lists(list("a" = 1, "b" = 2), list("b" = 3, "c" = 4, "d" = NULL))

Description

This function provides verbose information about absolute or relative element occurrences in data.frame columns.

Usage

occurrence_info(x, relative = FALSE, named = FALSE)

Arguments

Value

A character().

See Also

Other data.frame helpers: delete_columns_data.frame(), group_data.frame(), round_data.frame()

Examples

occurrence_info(datasets::warpbreaks, relative = FALSE, named = TRUE)

Description

This function creates a basic R package logo. The logo has a white background and the package name (with or without curly brackets) in the center. The font size for the package name is scaled such that it fits inside the logo. Type ?oeli to see an example.

Usage

package_logo(
  package_name,
  brackets = FALSE,
  background = ggplot2::ggplot() + ggplot2::theme_void(),
  s_x = 1,
  s_y = 1,
  s_width = 1,
  s_height = 1,
  white_around_sticker = FALSE
)

Arguments

Value

A ggplot object.

References

• This function builds upon sticker.

• Use use_logo to set up the logo for a package.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_namespace_calls(), find_pkg_functions(), identical_structure(), input_check_response(), match_arg(), print_data.frame(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

print(package_logo("my_package", brackets = TRUE))

Description

This function creates all permutations of a given vector.

Usage

permutations(x)

Arguments

Value

A list of all permutations of x.

References

Modified version of https://stackoverflow.com/a/20199902/15157768.

See Also

Other vector helpers: check_numeric_vector(), check_probability_vector(), chunk_vector(), equidistant_vectors(), insert_vector_entry(), map_indices(), match_numerics(), split_vector_at(), subsets(), vector_occurrence()

Examples

permutations(1:3)
permutations(LETTERS[1:3])

Description

This function prints a (possibly abbreviated) data.frame.

Usage

print_data.frame(
  x,
  rows = NULL,
  cols = NULL,
  digits = NULL,
  row.names = TRUE,
  col.names = TRUE
)

Arguments

Value

Invisibly returns x.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_namespace_calls(), find_pkg_functions(), identical_structure(), input_check_response(), match_arg(), package_logo(), print_matrix(), system_information(), unexpected_error(), user_confirm()

Examples

x <- data.frame(1:10, LETTERS[1:10], stats::rnorm(10))
print_data.frame(x, rows = 7)
print_data.frame(x, rows = 7, cols = 2)
print_data.frame(x, rows = 7, cols = 2, digits = 1)
print_data.frame(x, rows = 7, cols = 2, digits = 1, row.names = FALSE)
print_data.frame(x, rows = 7, cols = 2, digits = 1, col.names = FALSE)

Description

This function prints a (possibly abbreviated) matrix.

Usage

print_matrix(
  x,
  rowdots = 4,
  coldots = 4,
  digits = 2,
  label = NULL,
  simplify = FALSE,
  details = !simplify
)

Arguments

Value

Invisibly returns x.

References

This function is a modified version of pprint() from the {ramify} package.

See Also

Other package helpers: Dictionary, Storage, check_missing(), find_namespace_calls(), find_pkg_functions(), identical_structure(), input_check_response(), match_arg(), package_logo(), print_data.frame(), system_information(), unexpected_error(), user_confirm()

Examples

print_matrix(x = 1, label = "single numeric")
print_matrix(x = LETTERS[1:26], label = "letters")
print_matrix(x = 1:3, coldots = 2)
print_matrix(x = matrix(rnorm(99), ncol = 1), label = "single column matrix")
print_matrix(x = matrix(1:100, nrow = 1), label = "single row matrix")
print_matrix(x = matrix(LETTERS[1:24], ncol = 6), label = "big matrix")
print_matrix(x = diag(5), coldots = 2, rowdots = 2, simplify = TRUE)

Description

This function silences warnings, messages and any cat() or print() output from R expressions or functions.

Usage

quiet(x, print_cat = TRUE, message = TRUE, warning = TRUE)

Arguments

Value

Invisibly the expression x.

References

This function is a modified version of quiet.

See Also

Other function helpers: do.call_timed(), function_arguments(), function_body(), function_defaults(), timed(), try_silent(), variable_name()

Examples

f <- function() {
  warning("warning")
  message("message")
  cat("cat")
  print("print")
}
quiet(f())

Description

This function rounds (only) the numeric columns of a data.frame.

Usage

round_data.frame(df, digits = 0)

Arguments

Value

A data.frame.

See Also

Other data.frame helpers: delete_columns_data.frame(), group_data.frame(), occurrence_info()

Examples

df <- data.frame("label" = c("A", "B"), "number" = rnorm(10))
round_data.frame(df, digits = 1)

Description

This function samples a correlation matrix by sampling a covariance matrix from an inverse Wishart distribution and transforming it to a correlation matrix.

Usage

sample_correlation_matrix(dim, df = dim, scale = diag(dim))

Arguments

Value

A correlation matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_covariance_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

sample_correlation_matrix(dim = 3)

Description

This function samples a covariance matrix from an inverse Wishart distribution.

Usage

sample_covariance_matrix(dim, df = dim, scale = diag(dim), diag = FALSE)

Arguments

Value

A covariance matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_transition_probability_matrix(), stationary_distribution()

Examples

sample_covariance_matrix(dim = 3)

Description

This function returns a random, squared matrix of dimension dim that fulfills the properties of a transition probability matrix.

Usage

sample_transition_probability_matrix(dim, state_persistent = TRUE)

Arguments

Value

A transition probability matrix.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), stationary_distribution()

Examples

sample_transition_probability_matrix(dim = 3)

Description

This function simulates a Markov chain.

Usage

simulate_markov_chain(Gamma, T, delta = oeli::stationary_distribution(Gamma))

Arguments

Value

A numeric vector of length T with states.

See Also

Other simulation helpers: Simulator, correlated_regressors(), ddirichlet_cpp(), dmixnorm_cpp(), dmvnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), gaussian_tv()

Examples

Gamma <- matrix(c(0.8, 0.2, 0.3, 0.7), byrow = TRUE, nrow = 2)
delta <- c(0.6, 0.4)
simulate_markov_chain(Gamma = Gamma, T = 20, delta = delta)

Description

Creates a simulation setup, where a function f is evaluated runs times, optionally at each combination of input values.

Provides some convenience (see below for more details):

• Simulation results can be restored from a backup if the R session crashes.

• More simulation runs can be conducted after the initial simulation, failed simulation cases can be re-run.

• Parallel computation and progress updates are supported.

Details

Backup

Simulation results can be saved to disk, allowing you to restore the results if the R session is interrupted or crashes before the simulation completes. To enable backup, set backup = TRUE in the ⁠$go()⁠ method, which will create a backup directory at the location specified by path. To restore, use Simulator$initialize(use_backup = path).

More runs and re-run

If additional simulation runs are needed, simply call the ⁠$go()⁠ method again. Any cases that were not successfully completed in previous runs will be attempted again.

Parallel computation

By default, simulations run sequentially. But since they are independent, they can be parallelized to decrease computation time. To enable parallel computation, use the {future} framework. For example, run

future::plan(future::multisession, workers = 4)

in advance for computation in 4 parallel R sessions.

Progress updates

Use the {progressr} framework to get progress updates. For example, run the following in advance:

progressr::handlers(global = TRUE)
progressr::handlers(
  progressr::handler_progress(format = ">> :percent, :eta to go :message")
)

Active bindings

Methods

Public methods

• Simulator$new()

• Simulator$print()

• Simulator$define()

• Simulator$go()

Method new()

Initialize a Simulator object, either a new one or from backup.

Usage

Simulator$new(
  use_backup = NULL,
  verbose = getOption("verbose", default = FALSE)
)

Arguments

Method print()

Print method.

Usage

Simulator$print()

Method define()

Define function and arguments for a new Simulator object.

Usage

Simulator$define(f, ...)

Arguments

Method go()

Run simulations.

Usage

Simulator$go(
  runs = 0,
  backup = FALSE,
  path = paste0("backup_", format(Sys.time(), "%Y-%m-%d-%H-%M-%S"))
)

Arguments

See Also

Other simulation helpers: correlated_regressors(), ddirichlet_cpp(), dmixnorm_cpp(), dmvnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

# 1. Initialize a new simulation setup:
object <- Simulator$new(verbose = TRUE)

# 2. Define function `f` and arguments (if any):
f <- function(x, y = 1) {
  Sys.sleep(runif(1)) # to see progress updates
  x + y
}
x_args <- list(1, 2)
object$define(f = f, x = x_args)
print(object)

# 3. Define 'future' and 'progress' (optional):
## Not run: 
future::plan(future::sequential)
progressr::handlers(global = TRUE)
## End(Not run)

# 4. Evaluate `f` `runs` times at each parameter combination:
path <- file.path(
  tempdir(),
  paste0("backup_", format(Sys.time(), "%Y-%m-%d-%H-%M-%S"))
)
object$go(runs = 2, backup = TRUE, path = path)

# 5. Access the results:
object$results

# 6. Check if cases are pending or if an error occurred:
object$cases

# 7. Restore simulation results from backup:
object_restored <- Simulator$new(use_backup = path)
print(object_restored)
## Not run: all.equal(object, object_restored)

# 8. Run more simulations and pending simulations (if any):
object_restored$go(runs = 2)

Description

This function splits a vector at specific positions.

Usage

split_vector_at(x, at)

Arguments

Value

A list.

References

Based on https://stackoverflow.com/a/19274414.

See Also

Other vector helpers: check_numeric_vector(), check_probability_vector(), chunk_vector(), equidistant_vectors(), insert_vector_entry(), map_indices(), match_numerics(), permutations(), subsets(), vector_occurrence()

Examples

x <- 1:10
split_vector_at(x, c(2, 3, 5, 7))

Description

This function computes the stationary distribution corresponding to a transition probability matrix.

Usage

stationary_distribution(tpm, soft_fail = FALSE)

Arguments

Value

A numeric vector.

See Also

Other matrix helpers: check_correlation_matrix(), check_covariance_matrix(), check_one_hot_matrix(), check_transition_probability_matrix(), cov_to_chol(), diff_cov(), insert_matrix_column(), matrix_diagonal_indices(), matrix_indices(), sample_correlation_matrix(), sample_covariance_matrix(), sample_transition_probability_matrix()

Examples

tpm <- matrix(0.05, nrow = 3, ncol = 3)
diag(tpm) <- 0.9
stationary_distribution(tpm)

Description

Provides a simple indexing interface for list elements based on R6. Basically, it allows to store items in a list and to regain them based on identifiers defined by the user.

Value

The output depends on the method:

• $new() returns a Storage object.

• $add(), $remove(), and $print() invisibly return the Storage object (to allow for method chaining)

• $get() returns the requested element(s)

• $number() returns an integer

• $indices() return an integer vector

Setting identifiers

An identifier is a character, typically a binary property. Identifiers can be negated by placing an exclamation mark ("!") in front of them. Identifiers that have been assigned to other elements previously do not need to be specified again for new elements; instead, a default value can be used. This default value can be defined either globally for all cases (via the $missing_identifier field) or separately for each specific case (via the method argument).

User confirmation

If desired, the user can be asked for confirmation when adding, extracting, or removing elements using identifiers. This behavior can be set globally through the $confirm field or customized separately for each specific case via the method argument.

Active bindings

Methods