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oeli: Some Utilities for Developing Data Science Software

Description

A collection of general-purpose helper functions that I (and maybe others) find useful when developing data science software. Includes tools for simulation, data transformation, input validation, and more.

Author(s)

Maintainer: Lennart Oelschlägeroelschlaeger.lennart@gmail.com

See Also

Useful links:

• https://github.com/loelschlaeger/oeli

• http://loelschlaeger.de/oeli/

• Report bugs athttps://github.com/loelschlaeger/oeli/issues

Dictionary R6 Object

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()

Methodnew()

Initializing a newDictionary 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

Methodadd()

Adding an element to the dictionary.

Usage

Dictionary$add(...)

Arguments

Methodget()

Getting elements from the dictionary.

Usage

Dictionary$get(key, value = NULL)

Arguments

Methodremove()

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

Usage

Dictionary$remove(key)

Arguments

Methodprint()

Printing details of the dictionary.

Usage

Dictionary$print()

See Also

Other package helpers: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

# Managing variable metadata for a datasetmeta_dict <- Dictionary$new(  key_name = "var_name",  alias_name = "category",  value_names = c("label", "type"),  value_assert = alist(    label = checkmate::assert_string(),    type = checkmate::assert_choice(choices = c("numeric", "factor", "character"))  ),  allow_overwrite = FALSE,  keys_reserved = c("id"),  alias_choices = c("demographics", "outcome", "other"),  dictionary_name = "Variable Metadata")# Add entries to the dictionarymeta_dict$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 dictionarymeta_dict$print()# Retrieve full metadata for a variablemeta_dict$get("income")# Retrieve a specific piece of metadatameta_dict$get("income", value = "label")# Show variables by categorymeta_dict$alias

Simulator R6 Object

Description

Creates a simulation setup, where a functionf is evaluatedruns 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, failedsimulation 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 resultsif the R session is interrupted or crashes before the simulation completes.To enable backup, setbackup = TRUE in the⁠$go()⁠ method, which willcreate a backup directory at the location specified bypath.To restore, useSimulator$initialize(use_backup = path).

More runs and re-run

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

Parallel computation

By default, simulations run sequentially. But since they are independent,they can be parallelized to decrease computation time. To enable parallelcomputation, 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 toget 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()

Methodnew()

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

Usage

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

Arguments

Methodprint()

Print method.

Usage

Simulator$print()

Methoddefine()

Define function and arguments for a newSimulator object.

Usage

Simulator$define(f, ...)

Arguments

Methodgo()

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 (backup is optional):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)

Storage R6 Object

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 onidentifiers defined by the user.

Value

The output depends on the method:

• $new() returns aStorage object.

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

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

• $number() returns aninteger

• $indices() return anintegervector

Setting identifiers

An identifier is acharacter, typically a binary property. Identifierscan be negated by placing an exclamation mark ("!") in front of them.Identifiers that have been assigned to other elements previously do not needto 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 (viathe 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 globallythrough the$confirm field or customized separately for each specificcase via the method argument.

Active bindings

Methods

Public methods

• Storage$new()

• Storage$add()

• Storage$get()

• Storage$remove()

• Storage$number()

• Storage$indices()

• Storage$print()

Methodnew()

Initializing aStorage object.

Usage

Storage$new()

Methodadd()

Adding an element.

Usage

Storage$add(  x,  identifier,  confirm = interactive() & self$confirm,  missing_identifier = self$missing_identifier)

Arguments

Methodget()

Getting elements.

Usage

Storage$get(  identifier = character(),  ids = integer(),  logical = "and",  confirm = interactive() & self$confirm,  missing_identifier = self$missing_identifier,  id_names = FALSE)

Arguments

Methodremove()

removing elements

Usage

Storage$remove(  identifier = character(),  ids = integer(),  logical = "and",  confirm = interactive() & self$confirm,  missing_identifier = self$missing_identifier,  shift_ids = TRUE)

Arguments

Methodnumber()

Computing the number of identified elements.

Usage

Storage$number(  identifier = "all",  missing_identifier = self$missing_identifier,  logical = "and",  confirm = FALSE)

Arguments

Methodindices()

Returning indices based on defined identifiers.

Usage

Storage$indices(  identifier = "all",  logical = "and",  confirm = interactive() & self$confirm)

Arguments

Methodprint()

Printing details of the saved elements.

Usage

Storage$print(...)

Arguments

See Also

Other package helpers:Dictionary,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

### 1. Create a `Storage` object:my_storage <- Storage$new()# 2. Add elements along with identifiers:my_storage$  add(42, c("number", "rational"))$  add(pi, c("number", "!rational"))$  add("fear of black cats", c("text", "!rational"))$  add("wearing a seat belt", c("text", "rational"))$  add(mean, "function")# 3. What elements are stored?print(my_storage)# 4. Extract elements based on identifiers:my_storage$get("rational")my_storage$get("!rational")my_storage$get(c("text", "!rational"))my_storage$get("all") # get all elementsmy_storage$get(c("text", "!text"))my_storage$get(c("text", "!text"), logical = "or")# 5. Extract elements based on ids:my_storage$get(ids = 4:5)my_storage$get(ids = 4:5, id_names = TRUE) # add the ids as names

Check correlation matrix

Description

These functions check whether the input fulfills the properties of acorrelation 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 incheck_matrix.

See Also

Other matrix helpers: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

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)

Check covariance matrix

Description

These functions check whether the input fulfills the properties of acovariance 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 incheck_matrix.

See Also

Other matrix helpers:check_correlation_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)

Check list of lists

Description

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

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 incheck_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)

Check missing formal argument

Description

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

Usage

check_missing(x)assert_missing(x)test_missing(x)

Arguments

Value

Depending on the function prefix:

• If the check is successful,assert_missing() returnsx invisibly,whereascheck_missing() andtest_missing() returnTRUE.

• If the check is not successful,assert_missing() throws an error message,test_missing() returnsFALSE, andcheck_missing() returns a string withthe error message.

See Also

Other package helpers:Dictionary,Storage,find_namespace_calls(),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)

Check numeric vector

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 incheck_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)

Check probability vector

Description

These functions check whether the input fulfills the properties of aprobability 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 incheck_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)

Check transition probability matrix

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 incheck_matrix.

See Also

Other matrix helpers:check_correlation_matrix(),check_covariance_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)

Split a vector into chunks

Description

This function either

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

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

Usage

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

Arguments

Value

Alist.

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

Simulate correlated regressor values

Description

This function simulates regressor values from various marginal distributionswith custom correlations.

Usage

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

Arguments

Value

Adata.frame withn rows andlength(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 <- 100marginals <- 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)

Cholesky root of covariance matrix

Description

These functions compute the Cholesky root elements of a covariance matrixand, 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

Forcov_to_chol anumericvector of Cholesky rootelements.

Forchol_to_cov a covariancematrix.

See Also

Other matrix helpers:check_correlation_matrix(),check_covariance_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))

Dirichlet distribution

Description

The functionddirichlet() computes the density of a Dirichlet distribution.

The functionrdirichlet() 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

Forddirichlet(): The density value.

Forrdirichlet(): Ifn = 1 avector of lengthp, elseamatrix of dimensionn timesp 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 densityddirichlet(x = x, concentration = concentration)ddirichlet(x = x, concentration = concentration, log = TRUE)# samplerdirichlet(concentration = 1:3)rdirichlet(n = 4, concentration = 1:2)

Deletingdata.frame columns

Description

This function deletes columns of adata.frame by name.

Usage

delete_columns_data.frame(df, column_names)

Arguments

Value

The inputdf without the columns defined bycolumn_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"))

Difference and un-difference covariance matrix

Description

These functions difference and un-difference random vectors and covariancematrices.

Usage

diff_cov(cov, ref = 1)undiff_cov(cov_diff, ref = 1)delta(ref = 1, dim)M(ranking = seq_len(dim), dim)

Arguments

Details

Assumex \sim N(0, \Sigma) is a multivariate normally distributedrandom vector of dimensionn. We may want to consider the differencedvector

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

excludingthekth 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 referencerowk. More precise,\Delta_k is the identity matrix of dimensionn without rowk and with-1s in columnk.The difference operator\Delta_k can be computed viadelta(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 rowk = 1,\dots,n.The differenced covariance matrix\tilde \Sigma can be computed viadiff_delta(Sigma, ref = k).

Since\Delta_k is a non-bijective mapping,\Sigma cannot beuniquely restored from\tilde \Sigma. However, it is possible tocompute a non-unique solution\Sigma_0, such that\Delta_k \Sigma_0 \Delta_k = \tilde \Sigma. For such a non-uniquesolution, we add a column and a row of zerosat column and row numberk to\tilde{\Sigma}, respectively.An "un-differenced" covariance matrix\Sigma_0 can be computed viaundiff_delta(Sigma_diff, ref = k).

As a alternative to\Delta_k, the functionM() returns a matrix fortaking differences such that the resulting vector is negative.

Value

A (differenced or un-differenced) covariancematrix.

See Also

Other matrix helpers:check_correlation_matrix(),check_covariance_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 <- 4Sigma <- sample_covariance_matrix(dim = n)k <- 2x <- c(1, 3, 2, 4)# build difference operatordelta_k <- delta(ref = k, dim = n)# difference vectordelta_k %*% x# difference Sigma(Sigma_diff <- diff_cov(Sigma, ref = k))# un-difference Sigma(Sigma_0 <- undiff_cov(Sigma_diff, ref = k))# difference againSigma_diff_2 <- diff_cov(Sigma_0, ref = k)all.equal(Sigma_diff, Sigma_diff_2)# difference such that the resulting vector is negativeM(ranking = order(x, decreasing = TRUE), dim = n) %*% x

Mixture of normal distributions

Description

The functiondmixnorm() computes the density of a mixture of multivariatenormal distribution.

The functionpmixnorm() computes the cumulative distribution function of amixture of multivariate normal distribution.

The functionrmixnorm() samples from a mixture of multivariate normaldistribution.

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

The univariate normal mixture is available as the special casep = 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 onmvtnorm::pmvnorm with the randomizedQuasi-Monte-Carlo procedure by Genz and Bretz. The argumentabseps controlsthe accuracy of the Gaussian integral approximation.

Value

Fordmixnorm(): The density value.

Forpmixnorm(): The value of the distribution function.

Forrmixnorm(): Ifn = 1 avector of lengthp (notethat it is a column vector forrmixnorm_cpp()), elseamatrix of dimensionn timesp 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 densitydmixnorm(x = x, mean = mean, Sigma = Sigma, proportions = proportions)# compute CDFpmixnorm(x = x, mean = mean, Sigma = Sigma, proportions = proportions)# samplermixnorm(n = 3, mean = mean, Sigma = Sigma, proportions = proportions)

Multivariate normal distribution

Description

The functiondmvnorm() computes the density of a multivariate normaldistribution.

The functionpmvnorm() computes the cumulative distribution function of amultivariate normal distribution.

The functionrmvnorm() 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 casep = 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 callsmvtnorm::pmvnorm with the randomizedQuasi-Monte-Carlo procedure by Genz and Bretz. The argumentabseps controlsthe accuracy of the Gaussian integral approximation.

Value

Fordmvnorm(): The density value.

Forpmvnorm(): The value of the distribution function.

Forrmvnorm(): Ifn = 1 avector of lengthp (notethat it is a column vector forrmvnorm_cpp()), elseamatrix of dimensionn timesp 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 densitydmvnorm(x = x, mean = mean, Sigma = Sigma)dmvnorm(x = x, mean = mean, Sigma = Sigma, log = TRUE)# compute CDFpmvnorm(x = x, mean = mean, Sigma = Sigma)# samplermvnorm(n = 3, mean = mean, Sigma = Sigma)rmvnorm(mean = mean, Sigma = Sigma, log = TRUE)

Measure computation time

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 fordo.call.

Value

A list of the two elements"result" (the results of thedo.callcall) 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)

Truncated normal distribution

Description

The functiondtnorm() computes the density of a truncated normaldistribution.

The functionrtnorm() samples from a truncated normal distribution.

The functiondttnorm() andrttnorm() compute the density and sample froma 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

Fordtnorm() anddttnorm(): The density value.

Forrtnorm() andrttnorm(): 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 densitydmvnorm(x = x, mean = mean, Sigma = Sigma)dmvnorm(x = x, mean = mean, Sigma = Sigma, log = TRUE)# samplermvnorm(n = 3, mean = mean, Sigma = Sigma)rmvnorm(mean = mean, Sigma = Sigma, log = TRUE)

Wishart distribution

Description

The functiondwishart() computes the density of a Wishart distribution.

The functionrwishart() 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

Fordwishart(): The density value.

Forrwishart(): Amatrix, 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 <- 6scale <- matrix(c(1, -0.3, -0.3, 0.8), ncol = 2)# compute densitydwishart(x = x, df = df, scale = scale)dwishart(x = x, df = df, scale = scale, log = TRUE)dwishart(x = x, df = df, scale = scale, inv = TRUE)# samplerwishart(df = df, scale = scale)rwishart(df = df, scale = scale, inv = TRUE)# expectation of Wishart is df * scalen <- 100replicate(n, rwishart(df = df, scale = scale), simplify = FALSE) |>  Reduce(f = "+") / ndf * scale# expectation of inverse Wishart is scale / (df - p - 1)n <- 100replicate(n, rwishart(df = df, scale = scale, TRUE), simplify = FALSE) |>  Reduce(f = "+") / nscale / (df - 2 - 1)

Generate equidistant vectors in Euclidean space

Description

This function constructs the coordinates of vertices of a regular simplexin\mathbb{R}^{\code{dim}} and returns the firstn of them,

• scaled so that the pairwise Euclidean distance between any two verticesequalsdist,

• and centered so their centroid is atcenter.

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))

Namespace calls

Description

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

Usage

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

Arguments

Value

Adata.frame. Ifas_list = TRUE, alist.

See Also

Other package helpers:Dictionary,Storage,check_missing(),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)

Get function arguments

Description

This function returns the names of function arguments.

Usage

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

Arguments

Value

Acharacter 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)

Extract function body

Description

This function extracts the body of a function as a singlecharacter.

Usage

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

Arguments

Value

Acharacter, the body off.

See Also

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

Examples

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

Get default function arguments

Description

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

Usage

function_defaults(f, exclude = NULL)

Arguments

Value

A namedlist.

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")

Gaussian total variation

Description

Computes the total variation (TV) between two multivariate Gaussiandistributionsf_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 casemean1 <- 0mean2 <- 1Sigma1 <- Sigma2 <- matrix(1)gaussian_tv(mean1, mean2, Sigma1, Sigma2)### bivariate casemean1 <- 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)

Grouping of adata.frame

Description

This function groups adata.frame according to values of one column.

Usage

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

Arguments

Value

Alist ofdata.frames, subsets ofdf.

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)

Check if two objects have identical structure

Description

This function determines whether two objects have the same structure,

• which includes themode,class anddimension

• but doesnot include concrete values or attributes.

Usage

identical_structure(x, y)

Arguments

Value

EitherTRUE ifx andy have the same structure, andFALSE, else.

References

Inspired byhttps://stackoverflow.com/a/45548885/15157768.

See Also

Other package helpers:Dictionary,Storage,check_missing(),find_namespace_calls(),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)))

Standardized response to input check

Description

This function provides a standardized response to input checks, ensuringconsistency.

Usage

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

Arguments

Value

TRUE ifcheck isTRUE (or any element incheck isTRUE, ifcheckis alist) . Else, depending onerror:

• Iferror isTRUE, throws an error.

• Iferror isFALSE, returnsFALSE.

See Also

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

Examples

x <- "1"y <- 1### check is successfulinput_check_response(  check = checkmate::check_character(x),  var_name = "x",  error = TRUE)### alternative checksinput_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)

Insert column in matrix

Description

This function inserts a column into a matrix.

Usage

insert_matrix_column(A, x, p)

Arguments

Value

Amatrix.

See Also

Other matrix helpers:check_correlation_matrix(),check_covariance_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 <- 1:3insert_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 valuex <- 2insert_matrix_column(A, x, 0)### also multiple positionsinsert_matrix_column(A, x, 0:3)### also trivial caseinsert_matrix_column(matrix(nrow = 0, ncol = 0), integer(), integer())

Insert entry in vector

Description

This function inserts a value into a vector.

Usage

insert_vector_entry(v, x, p)

Arguments

Value

Avector.

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 <- 1:3x <- 0insert_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 positionsinsert_vector_entry(v, x, 0:3)### also trivial caseinsert_vector_entry(integer(), integer(), integer())

Map indices

Description

This function maps indices from an input vector to corresponding sequences ofgrouped indices. Each element from the input specifies a group to be mappedfrom the sequence, determined by the grouping sizen.

Usage

map_indices(indices, n)

Arguments

Details

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

Value

Anintegervector, containing the mapped indices according to thespecified 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 3map_indices(c(1, 3, 5), 3)

Argument matching

Description

This function matches function arguments and is a modified version ofmatch.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 isone. Otherwise, an error is signaled ifseveral.ok isFALSEornone.ok isFALSE.Whenseveral.ok isTRUE and (at least) one element ofarg has a match, all un-abbreviated versions of matches are returned.Whennone.ok isTRUE andarg has zero elements,character(0) is returned.

See Also

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

Best-possible match of two numeric vectors

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

Aninteger vector of lengthlength(x) with the positions ofy inx.

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)

Get indices of matrix diagonal

Description

This function returns the indices of the diagonal elements of a quadraticmatrix.

Usage

matrix_diagonal_indices(n, triangular = NULL)

Arguments

Value

Anintegervector.

See Also

Other matrix helpers:check_correlation_matrix(),check_covariance_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 elementsn <- 3matrix(1:n^2, n, n)matrix_diagonal_indices(n)# indices of diagonal elements of lower-triangular matrixL <- matrix(0, n, n)L[lower.tri(L, diag=TRUE)] <- 1:((n * (n + 1)) / 2)Lmatrix_diagonal_indices(n, triangular = "lower")# indices of diagonal elements of upper-triangular matrixU <- matrix(0, n, n)U[upper.tri(U, diag=TRUE)] <- 1:((n * (n + 1)) / 2)Umatrix_diagonal_indices(n, triangular = "upper")

Getmatrix indices

Description

This function returnsmatrix indices ascharacter.

Usage

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

Arguments

Value

Acharactervector.

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(),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)

Merge named lists

Description

This function mergeslists based on their element names. Elements areonly included in the final outputlist, if no formerlist hascontributed an element with the same name.

Usage

merge_lists(...)

Arguments

Value

Alist.

See Also

Other list helpers:check_list_of_lists()

Examples

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

Provide information about occurrences

Description

This function provides verbose information about absolute or relativeelement occurrences indata.frame columns.

Usage

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

Arguments

Value

Acharacter().

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)

Creating a basic logo for an R package

Description

This function creates a basic R package logo. The logo has a whitebackground and the package name (with or without curly brackets) in thecenter. The font size for the package name is scaled such that it fits insidethe 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

Aggplot object.

References

• This function builds uponsticker.

• Useuse_logo to set up the logo for a package.

See Also

Other package helpers:Dictionary,Storage,check_missing(),find_namespace_calls(),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))

Build permutations

Description

This function creates all permutations of a givenvector.

Usage

permutations(x)

Arguments

Value

Alist of all permutations ofx.

References

Modified version ofhttps://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])

Print (abbreviated)data.frame

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 returnsx.

See Also

Other package helpers:Dictionary,Storage,check_missing(),find_namespace_calls(),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)

Print (abbreviated)matrix

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 returnsx.

References

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

See Also

Other package helpers:Dictionary,Storage,check_missing(),find_namespace_calls(),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)

Silence R code

Description

This function silences warnings, messages and anycat() orprint()output from R expressions or functions.

Usage

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

Arguments

Value

Invisibly the expressionx.

References

This function is a modified version ofquiet.

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())

Roundnumeric columns of adata.frame

Description

This function rounds (only) thenumeric columns of adata.frame.

Usage

round_data.frame(df, digits = 0)

Arguments

Value

Adata.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)

Sample correlation matrix

Description

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

Usage

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

Arguments

Value

A correlationmatrix.

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_covariance_matrix(),sample_transition_probability_matrix(),stationary_distribution()

Examples

sample_correlation_matrix(dim = 3)

Sample covariance matrix

Description

This function samples a covariance matrix from an inverse Wishartdistribution.

Usage

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

Arguments

Value

A covariancematrix.

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_transition_probability_matrix(),stationary_distribution()

Examples

sample_covariance_matrix(dim = 3)

Sample transition probability matrices

Description

This function returns a random, squared matrix of dimensiondimthat fulfills the properties of a transition probability matrix.

Usage

sample_transition_probability_matrix(dim, state_persistent = TRUE)

Arguments

Value

A transition probabilitymatrix.

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(),stationary_distribution()

Examples

sample_transition_probability_matrix(dim = 3)

Simulate Markov chain

Description

This function simulates a Markov chain.

Usage

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

Arguments

Value

Anumeric vector of lengthT 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)

Split a vector at positions

Description

This function splits a vector at specific positions.

Usage

split_vector_at(x, at)

Arguments

Value

Alist.

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

Stationary distribution

Description

This function computes the stationary distribution corresponding to atransition probability matrix.

Usage

stationary_distribution(tpm, soft_fail = FALSE)

Arguments

Value

Anumeric vector.

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()

Examples

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

Generate vector subsets

Description

This function generates subsets of a vector.

Usage

subsets(v, n = seq_along(v))

Arguments

Value

Alist, each element is a subset ofv.

See Also

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

Examples

v <- 1:3subsets(v)subsets(v, c(1, 3)) # only subsets of length 1 or 3subsets(integer())  # trivial case works

General system level information

Description

This function returns alist of general system level information.

Usage

system_information()

Value

Alist with elements:

• maschine, the model name of the device

• cores, the number of cores

• ram, the size of the RAM

• os, the operating system

• rversion, the R version used

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(),unexpected_error(),user_confirm()

Examples

system_information()

Interrupt long evaluations

Description

This function interrupts an evaluation after a certain number of seconds.Note the limitations documented insetTimeLimit.

Usage

timed(expression, seconds = Inf, on_time_out = "silent")

Arguments

Value

The value ofexpression or, if the evaluation time exceeded, whateveris specified foron_time_out.

See Also

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

Examples

foo <- function(x) {  for (i in 1:10) Sys.sleep(x / 10)  return(x)}timed(foo(0.5), 1)timed(foo(1.5), 1)

Try an expression silently

Description

This function tries to executeexpr and returns a string with theerror message if the execution failed.

Usage

try_silent(expr)

Arguments

Details

This function is a wrapper fortry.

Value

Either the value ofexpr or in case of a failure an object of classfail, which contains the error message.

See Also

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

Examples

## Not run: try_silent(1 + 1)try_silent(1 + "1")## End(Not run)

Handling of an unexpected error

Description

This function reacts to an unexpected error by throwing an error and linkingto an issue site with the request to submit an issue.

Usage

unexpected_error(  msg = "Ups, an unexpected error occured.",  issue_link = "https://github.com/loelschlaeger/oeli/issues")

Arguments

Value

No return value, but it throws an error.

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(),user_confirm()

User confirmation

Description

This function asks in an interactive question a binary question.

Usage

user_confirm(question = "Question?", default = FALSE)

Arguments

Value

EitherTRUE orFALSE.

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()

Determine variable name

Description

This function tries to determine the name of a variable passed to afunction.

Usage

variable_name(variable, fallback = "unnamed")

Arguments

Value

Acharacter, the variable name.

See Also

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

Examples

variable_name(a)f <- function(x) variable_name(x)f(x = a)

Find the positions of first or last occurrence of unique vector elements

Description

This function finds the positions of first or last occurrence of uniquevector elements.

Usage

vector_occurrence(x, type = "first")

Arguments

Value

Anintegervector, the positions of the unique vector elements.The ordering corresponds tounique(x), i.e., thei-th element inthe output is the (first or last) occurrence of thei-th element fromunique(x).

See Also

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

Examples

x <- c(1, 1, 1, 2, 2, 2, 3, 3, 3)unique(x)vector_occurrence(x, "first")vector_occurrence(x, "last")