Type:	Package
Title:	Validation of Arguments and Objects in User-Defined Functions
Version:	1.0.3
Description:	Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.
License:	MIT + file LICENSE
URL:	https://ptfonseca.github.io/inspector/, https://github.com/ptfonseca/inspector
BugReports:	https://github.com/ptfonseca/inspector/issues
Encoding:	UTF-8
Depends:	R (≥ 2.10)
Imports:	Rdpack (≥ 0.7)
Suggests:	covr (≥ 3.5.0), knitr (≥ 1.28), rmarkdown (≥ 2.1), testthat (≥ 2.3.2), pcal (≥ 1.0.0)
RdMacros:	Rdpack
RoxygenNote:	7.1.1
NeedsCompilation:	no
Packaged:	2021-06-17 18:34:03 UTC; pedro
Author:	Pedro Fonseca [aut, cre], FCT [fnd]
Maintainer:	Pedro Fonseca <pedro.teles.fonseca@phd.iseg.ulisboa.pt>
Repository:	CRAN
Date/Publication:	2021-06-17 19:00:02 UTC

Package 'inspector'

Description

The inspector package provides utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.

Details

For a complete list of functions see library(help = "inspector"). For instructions and examples see the "Usage" section of the README.

Author(s)

Maintainer: Pedro Fonseca pedro.teles.fonseca@phd.iseg.ulisboa.pt (ORCID)

Other contributors:

• FCT [funder]

See Also

Useful links:

• https://ptfonseca.github.io/inspector/

• https://github.com/ptfonseca/inspector

• Report bugs at https://github.com/ptfonseca/inspector/issues

Validate vectors of Bayes factors

Description

inspect_bfactor checks if an object is a numeric vector of valid Bayes factor values. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_bfactor(x, allow_nas = TRUE, warning_nas = TRUE)

Arguments

Details

inspect_bfactor conducts a series of tests to check if x is a numeric vector of valid Bayes factor values. Namely, inspect_bfactor checks if:

• x is NULL or empty.

• x is an atomic vector.

• x is numeric.

• x has NA or NaN values.

• The values of x are non-negative.

Value

inspect_bfactor does not return any output. There are three possible outcomes:

• The call is silent if:

  • x is a numeric vector of valid Bayes factor values and there are no NA or NaN values in x.

  • x is a numeric vector of valid Bayes factor values, there are some NA or NaN values in x, allow_nas is set to TRUE and warning_nas is set to FALSE.

• An informative warning message is given if x is a numeric vector of valid Bayes factor values, there are some NA or NaN values in x and both allow_nas and warning_nas are set to TRUE.

• An informative error message is thrown and the execution is stopped if:

  • x is not a numeric vector of valid Bayes factor values.

  • x is a numeric vector of valid Bayes factor values, there are some in NA or NaN values in x and allow_nas is set to FALSE.

See Also

• inspect_bfactor_log to check if an object is a numeric vector of valid logarithmic Bayes factor values.

• bfactor_interpret for the interpretation of Bayes factors.

• inspect_bfactor_scale to check if an object is a valid Bayes factor interpretation scale.

Examples

# Calls that pass silently:
x1 <- c(0, 0.5, 1, 10, 50, 100)
x2 <- c(NA, 0.5, 1, 10, 50, 100)
inspect_bfactor(x1)
inspect_bfactor(x2, warning_nas = FALSE)
inspect_bfactor(x2, allow_nas = TRUE, warning_nas = FALSE)

# Call that throws an informative warning message:
y <- c(0.1, 0.2, NA, 0.4, 0.5)
try(inspect_bfactor(y))
try(inspect_bfactor(y, warning_nas = TRUE))
try(inspect_bfactor(y, allow_nas = TRUE, warning_nas = TRUE))

# Calls that throw informative error messages:
z <- c(-0.9, 0, 0.1, 0.2, 0.3, 0.4, 0.5)
try(inspect_bfactor(z))
mylist <- list(
  NULL, TRUE, factor(.5), matrix(0.5),
  "0.5", list(0.5), NA, NaN, numeric(0), -0.5, -5
)
try(inspect_bfactor(mylist[[1]]))
try(inspect_bfactor(mylist[[2]]))
try(inspect_bfactor(mylist[[3]]))
try(inspect_bfactor(mylist[[4]]))
try(inspect_bfactor(mylist[[5]]))
try(inspect_bfactor(mylist[[6]]))
try(inspect_bfactor(mylist[[7]]))
try(inspect_bfactor(mylist[[8]]))
try(inspect_bfactor(mylist[[9]]))
try(inspect_bfactor(mylist[[10]]))
try(inspect_bfactor(mylist[[11]]))

Validate vectors of logarithmic Bayes factors

Description

inspect_bfactor_log checks if an object is a numeric vector of valid logarithmic Bayes factor values. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_bfactor_log(x, allow_nas = TRUE, warning_nas = TRUE)

Arguments

Details

inspect_bfactor_log conducts a series of tests to check if x is a numeric vector of valid logarithmic Bayes factor values. Namely, inspect_bfactor_log checks if:

• x is NULL or empty.

• x is an atomic vector.

• x is numeric.

• x has NA or NaN values.

Value

inspect_bfactor_log does not return any output. There are three possible outcomes:

• The call is silent if:

  • x is a numeric vector of valid logarithmic Bayes factor values and there are no NA or NaN values in x.

  • x is a numeric vector of valid logarithmic Bayes factor values, there are some NA or NaN values in x, allow_nas is set to TRUE and warning_nas is set to FALSE.

• An informative warning message is given if x is a numeric vector of valid logarithmic Bayes factor values, there are some NA or NaN values in x and both allow_nas and warning_nas are set to TRUE.

• An informative error message is thrown and the execution is stopped if:

  • x is not a numeric vector of valid logarithmic Bayes factor values.

  • x is a numeric vector of valid logarithmic Bayes factor values, there are some NA or NaN values in x and allow_nas is set to FALSE.

See Also

• inspect_bfactor to check if an object is a numeric vector of valid Bayes factor values.

• bfactor_log_interpret for the interpretation of the logarithms of Bayes factors.

• inspect_bfactor_scale to check if an object is a Bayes factor interpretation scale.

• inspect_log_base to check if an object is an eligible logarithmic base.

Examples

# Calls that pass silently:
x1 <- c(0, 0.5, 1, 10, 50, 100)
x2 <- c(NA, 0.5, 1, 10, 50, 100)
inspect_bfactor_log(x1)
inspect_bfactor_log(x2, warning_nas = FALSE)
inspect_bfactor_log(x2, allow_nas = TRUE, warning_nas = FALSE)

# Call that throws an informative warning message:
y <- c(0.1, 0.2, NA, 0.4, 0.5)
try(inspect_bfactor_log(y))
try(inspect_bfactor_log(y, warning_nas = TRUE))
try(inspect_bfactor_log(y, allow_nas = TRUE, warning_nas = TRUE))

# Calls that throw informative error messages:
mylist <- list(
  NULL, TRUE, factor(.5), matrix(0.5),
  "0.5", list(0.5), numeric(0), NA, NaN
)
try(inspect_bfactor_log(mylist[[1]]))
try(inspect_bfactor_log(mylist[[2]]))
try(inspect_bfactor_log(mylist[[3]]))
try(inspect_bfactor_log(mylist[[4]]))
try(inspect_bfactor_log(mylist[[5]]))
try(inspect_bfactor_log(mylist[[6]]))
try(inspect_bfactor_log(mylist[[7]]))
try(inspect_bfactor_log(mylist[[8]]))
try(inspect_bfactor_log(mylist[[9]]))

Validate Bayes factor interpretation scales

Description

inspect_bfactor_scale checks if an object is a character vector of length 1 that is eligible to represent one of the Bayes factor interpretation scales available in the pcal package. This can be useful to validate inputs in user-defined functions.

Usage

inspect_bfactor_scale(x)

Arguments

Details

inspect_bfactor_scale conducts a series of tests to check if x is a character vector of length 1 that is eligible to represent one of the Bayes factor interpretation scales available in the pcal package. Namely, inspect_bfactor_scale checks if:

• x is NULL or empty.

• x is NA or NaN.

• x is an atomic vector of length 1

• The typeof x is character

• The value of x is either "Jeffreys" or "Kass-Raftery" (not case sensitive).

Value

inspect_bfactor_scale does not return any output. There are two possible scenarios:

• The call is silent if x is a character vector of length 1 that is eligible to represent one of the Bayes factor interpretation scales available in the pcal package.

• An informative error message is thrown otherwise.

See Also

• bfactor_interpret for the interpretation of Bayes factors.

• bfactor_log_interpret for the interpretation of the logarithms of Bayes factors.

• inspect_bfactor to check if an object is a numeric vector of valid Bayes factor values.

• inspect_bfactor_log to check if an object is a numeric vector of valid logarithmic Bayes factor values.

Examples

# Calls that pass silently:
x1 <- "Jeffreys"
x2 <- "jeffreys"
x3 <- "kass-raftery"
x4 <- "Kass-Raftery"
inspect_bfactor_scale(x1)
inspect_bfactor_scale(x2)
inspect_bfactor_scale(x3)
inspect_bfactor_scale(x4)

# Calls that throw informative error messages:
mylist <- list(
  NULL, NA, NaN, 10, "Bayes", "Jeff",
  "kassraftery", c("jeffreys", "kass-raftery")
)
try(inspect_bfactor_scale(mylist[[1]]))
try(inspect_bfactor_scale(mylist[[2]]))
try(inspect_bfactor_scale(mylist[[3]]))
try(inspect_bfactor_scale(mylist[[4]]))
try(inspect_bfactor_scale(mylist[[5]]))
try(inspect_bfactor_scale(mylist[[6]]))
try(inspect_bfactor_scale(mylist[[7]]))
try(inspect_bfactor_scale(mylist[[8]]))

Validate factor levels

Description

inspect_categories checks if an object is eligible to be used as the levels of a factor. This can be useful to validate inputs in user-defined functions.

Usage

inspect_categories(x)

Arguments

Details

inspect_categories conducts a series of tests to check if x is eligible to be used as the levels of a factor. Namely, inspect_categories checks if:

• x is NULL or empty.

• x is atomic.

• x has an eligible data type (logical, integer, double, character).

• There are NA or NaN values in x.

• There are repeated values in x.

Value

inspect_categories does not return any output. There are two possible outcomes:

• The call is silent if x is eligible to be used as the levels of a factor.

• An informative error message is thrown otherwise.

See Also

• inspect_data_dichotomous to validate dichotomous data.

• inspect_data_categorical and inspect_data_cat_as_dichotom to validate categorical data.

• inspect_par_bernoulli to validate Bernoulli/Binomial proportions.

• inspect_par_multinomial to validate vectors of Multinomial proportions.

• inspect_character to validate character vectors.

• inspect_character_match to validate character vectors with predefined allowed values.

Examples

# Calls that pass silently:
x1 <- 1:5
x2 <- c("yes", "no")
x3 <- c(TRUE, FALSE)
x4 <- factor(c("smoker", "non-smoker"))
x5 <- factor(c("yes", "no", "yes"))
inspect_categories(x1)
inspect_categories(x2)
inspect_categories(x3)
inspect_categories(x4)
inspect_categories(levels(x5))

# Calls that throw informative error messages:
y1 <- c(1, 1:5)
y2 <- c("yes", "no", "yes")
y3 <- factor(c("yes", "no", "yes"))
try(inspect_categories(y1))
try(inspect_categories(y2))
try(inspect_categories(y3))
try(mylist <- list(
  NULL, numeric(0),
  complex(1), list(10), NaN, NA
))
try(inspect_categories(mylist[[1]]))
try(inspect_categories(mylist[[2]]))
try(inspect_categories(mylist[[3]]))
try(inspect_categories(mylist[[4]]))
try(inspect_categories(mylist[[5]]))
try(inspect_categories(mylist[[6]]))

Validate character vectors

Description

inspect_character checks if an object is a character vector. This can be useful to validate inputs in user-defined functions.

Usage

inspect_character(x, allow_nas = TRUE, warning_nas = FALSE)

Arguments

Details

inspect_character conducts a series of tests to check if x is a character vector. Namely, inspect_character checks if:

• x is NULL or empty.

• x is an atomic vector.

• The typeof x is character.

• There are NA or NaN values in x.

Value

inspect_character does not return any output. There are three possible outcomes:

• The call is silent if:

  • x is a character vector and there are no NA or NaN values in x.

  • x is a character vector, there are some NA or NaN values in x, allow_nas is set to TRUE and warning_nas is set to FALSE.

• An informative warning message is thrown if x is a character vector, there are some NA or NaN values in x and both allow_nas and warning_nas are set to TRUE.

• An informative error message is thrown if:

  • x is not a character vector.

  • x is a character vector, there are some NA or NaN values in x and allow_nas is set to FALSE.

See Also

• inspect_character_match to validate character vectors with predefined allowed values.

• inspect_true_or_false to check if an object is a non-missing logical value.

Examples

# Calls that pass silently:
x1 <- "Kass"
x2 <- c("Kass", "Raftery")
x3 <- c("Kass", "Raftery", NA)
x4 <- letters
inspect_character(x1)
inspect_character(x2)
inspect_character(x3)
inspect_character(x4)

# Call that throws an informative warning message
y <- c("Kass", "Raftery", NA)
try(inspect_character(y, warning_nas = TRUE))

# Calls that throw informative error messages
try(inspect_character(y, allow_nas = FALSE))
mylist <- list(
  NULL, character(0), 1,
  c(1, 2), factor(c(1, 2)), list(c(1, 2)), NaN, NA
)
try(inspect_character(mylist[[1]]))
try(inspect_character(mylist[[2]]))
try(inspect_character(mylist[[3]]))
try(inspect_character(mylist[[4]]))
try(inspect_character(mylist[[5]]))
try(inspect_character(mylist[[6]]))
try(inspect_character(mylist[[7]]))
try(inspect_character(mylist[[8]]))

Validate character values

Description

inspect_character_match checks if an object is a character vector of length 1 that belongs to a set of allowed values. This can be useful to validate inputs in user-defined functions.

Usage

inspect_character_match(x, allowed, case_sensitive = FALSE)

Arguments

Details

inspect_character_match conducts a series of tests to check if x is a character vector of length 1 whose value belongs to the set of allowed values. Namely, inspect_character_match checks if:

• x is NULL or empty.

• x is an atomic vector of length 1.

• The typeof x is character.

• x is NA or NaN.

• x is one of the allowed values (as specified in the allowed argument).

By default, the comparison of x with allowed is not case sensitive. If you only want case sensitive matches of x to allowed set case_sensitive to TRUE.

Value

inspect_character_match does not return any output. There are two possible outcomes:

• The call is silent if x is a character vector of length 1 whose value belongs to the set of allowed values.

• An informative error message is thrown otherwise.

See Also

• inspect_character to validate character vectors with arbitrary allowed values.

• inspect_true_or_false to check if an object is a non-missing logical value.

Examples

# Calls that pass silently:
x1 <- "Kass"
x2 <- "kass"
inspect_character_match(x1, allowed = c("Kass", "Raftery"))
inspect_character_match(x2, allowed = c("Kass", "Raftery"))

# Calls that throw informative error messages:
y1 <- "kasss"
y2 <- "kass"
try(inspect_character_match(y1, allowed = c("Kass", "Raftery")))
try(inspect_character_match(y2,
  allowed = c("Kass", "Raftery"),
  case_sensitive = TRUE
))
mylist <- list(
  NULL, character(0), c("abc", "abcd"),
  c("abc", "abc"), "ab", list("abc"), factor("abc"), NaN, NA
)
try(inspect_character_match(mylist[[1]], "abc"))
try(inspect_character_match(mylist[[2]], "abc"))
try(inspect_character_match(mylist[[3]], "abc"))
try(inspect_character_match(mylist[[4]], "abc"))
try(inspect_character_match(mylist[[5]], "abc"))
try(inspect_character_match(mylist[[6]], "abc"))
try(inspect_character_match(mylist[[7]], "abc"))
try(inspect_character_match(mylist[[8]], "abc"))
try(inspect_character_match(mylist[[9]], "abc"))

Validate categorical data as dichotomous

Description

inspect_data_cat_as_dichotom checks if an object contains valid categorical data that is eligible to be used as dichotomous data. This can be useful to validate inputs in user-defined functions.

Usage

inspect_data_cat_as_dichotom(
  data,
  success,
  allow_nas = TRUE,
  warning_nas = FALSE
)

Arguments

Details

inspect_data_cat_as_dichotom conducts a series of tests to check if data contains valid categorical data that is eligible to be used as dichotomous data. Namely, inspect_data_cat_as_dichotom checks if:

• data and success are NULL or empty.

• data and success are atomic and have an eligible data type (logical, integer, double, character).

• data and success have NA or NaN values.

• success has length 1.

• success is observed in data.

Value

inspect_data_cat_as_dichotom does not return any output. There are three possible outcomes:

• The call is silent if:

  • data contains valid categorical data that is eligible to be used as dichotomous data and there are no NA or NaN values in data.

  • data contains valid categorical data that is eligible to be used as dichotomous data, there are some NA or NaN values in data, allow_nas is set to TRUE and warning_nas is set to FALSE.

• An informative warning message is thrown if:

  • data contains valid categorical data that is eligible to be used as dichotomous data and success is not observed in data.

  • data contains valid categorical data that is eligible to be used as dichotomous data, there are NA or NaN values in data and both allow_nas and warning_nas are set to TRUE.

• An informative error message is thrown and the execution is stopped if:

  • data does not contain valid categorical data that is eligible to be used as dichotomous data.

  • data contains valid categorical data that is eligible to be used as dichotomous data, there are some NA or NaN values in data and allow_nas is set to FALSE.

See Also

• inspect_data_categorical to validate categorical.

• inspect_par_multinomial to validate vectors of Multinomial proportions.

• inspect_data_dichotomous to validate dichotomous data.

• inspect_par_bernoulli to validate Bernoulli/Binomial proportions.

Examples

# Calls that pass silently:
x1 <- c(1, 0, 0, 1, 0)
x2 <- c(FALSE, FALSE, TRUE)
x3 <- c("yes", "no", "yes")
x4 <- factor(c("yes", "no", "yes"))
x5 <- c(1, 0, 0, 1, 0, NA)
inspect_data_cat_as_dichotom(x1, success = 1)
inspect_data_cat_as_dichotom(x2, success = TRUE)
inspect_data_cat_as_dichotom(x3, success = "yes")
inspect_data_cat_as_dichotom(x4, success = "yes")
inspect_data_cat_as_dichotom(x5, success = 1)

# Calls that throw an informative warning message:
y1 <- c(1, 1, NA, 0, 0)
y2 <- c(0, 0)
success <- 1
try(inspect_data_cat_as_dichotom(y1, success = 1, warning_nas = TRUE))
try(inspect_data_cat_as_dichotom(y2, success = success))

# Calls that throw an informative error message:
try(inspect_data_cat_as_dichotom(y1, 1, allow_nas = FALSE))
try(inspect_data_cat_as_dichotom(NULL, 1))
try(inspect_data_cat_as_dichotom(c(1, 0), NULL))
try(inspect_data_cat_as_dichotom(list(1, 0), 1))
try(inspect_data_cat_as_dichotom(c(1, 0), list(1)))
try(inspect_data_cat_as_dichotom(numeric(0), 0))
try(inspect_data_cat_as_dichotom(1, numeric(0)))
try(inspect_data_cat_as_dichotom(NaN, 1))
try(inspect_data_cat_as_dichotom(NA, 1))
try(inspect_data_cat_as_dichotom(c(1, 0), NA))
try(inspect_data_cat_as_dichotom(c(1, 0), NaN))
try(inspect_data_cat_as_dichotom(c(1, 0), 2))

Validate categorical data

Description

inspect_data_categorical checks if an object contains data that is eligible to have been generated by a Multinomial distribution. This can be useful to validate inputs in user-defined functions.

Usage

inspect_data_categorical(data, allow_nas = TRUE, warning_nas = FALSE)

Arguments

Details

inspect_data_categorical conducts a series of tests to check if data is eligible to have been generated by a Multinomial distribution. Namely, inspect_data_categorical checks if:

• data is NULL or empty.

• data is atomic and have an eligible data type (logical, integer, double, character).

• data has NA or NaN values.

Value

inspect_data_categorical does not return any output. There are three possible outcomes:

• The call is silent if:

  • data is eligible to have been generated by a Multinomial distribution and there are no NA or NaN values in data.

  • data is eligible to have been generated by a Multinomial distribution, there are some NA or NaN values in data and warning_nas is set to FALSE.

• An informative warning message is thrown if: data is eligible to have been generated by a Multinomial distribution, there are some NA or NaN values in data and warning_nas is set to TRUE.

• An informative error message is thrown and the execution is stopped if:

  • data is not eligible to have been generated by a Multinomial distribution.

  • data is eligible to have been generated by a Multinomial distribution, there are some NA or NaN values in data and allow_nas is set to TRUE.

See Also

• inspect_data_cat_as_dichotom to validate categorical data as dichotomous.

• inspect_par_multinomial to validate vectors of Multinomial proportions.

• inspect_data_dichotomous to validate dichotomous data.

• inspect_par_bernoulli to validate Bernoulli/Binomial proportions.

Examples

# Calls that pass silently:
x1 <- c(1, 0, 0, 1, 2)
x2 <- c(FALSE, FALSE, TRUE, NA)
x3 <- c("yes", "no", "yes", "maybe")
x4 <- factor(c("yes", "no", "yes", "maybe"))
x5 <- c(1, 0, 0, 1, 0, NA, 2)
inspect_data_categorical(x1)
inspect_data_categorical(x2)
inspect_data_categorical(x3)
inspect_data_categorical(x4)
inspect_data_categorical(x5)
inspect_data_categorical(x5)

# Call that throws an informative warning message:
y1 <- c(1, 1, NA, 0, 0, 2)
try(inspect_data_categorical(y1, warning_nas = TRUE))

# Calls that throw an informative error message:
z <- c(1, 1, NA, 0, 0, 2)
try(inspect_data_categorical(z, allow_nas = FALSE))
try(inspect_data_categorical(NULL))
try(inspect_data_categorical(list(1, 0)))
try(inspect_data_categorical(numeric(0)))
try(inspect_data_categorical(NaN))
try(inspect_data_categorical(NA))

Validate dichotomous data

Description

inspect_data_dichotomous checks if an object contains data that is eligible to have been generated by a series of Bernoulli trials. This can be useful to validate inputs in user-defined functions.

Usage

inspect_data_dichotomous(data, success, allow_nas = TRUE, warning_nas = FALSE)

Arguments

Details

inspect_data_dichotomous conducts a series of tests to check if data is eligible to have been generated by a series of Bernoulli trials. Namely, inspect_data_dichotomous checks if:

• data and success are NULL or empty.

• data and success are atomic and have an eligible data type (logical, integer, double, character).

• data and success have NA or NaN values.

• The number of unique values in data and success are adequate.

• success has length 1.

• success is observed in data.

Value

inspect_data_dichotomous does not return any output. There are three possible outcomes:

• The call is silent if:

  • data is eligible to have been generated by a series of Bernoulli trials and there are no NA or NaN values in data.

  • data is eligible to have been generated by a series of Bernoulli trials, there are some NA or NaN values in data, allow_nas is set to TRUE and warning_nas is set to FALSE.

• An informative warning message is thrown if:

  • data is eligible to have been generated by a series of Bernoulli trials and success is not observed in data.

  • data is eligible to have been generated by a series of Bernoulli trials, there are NA or NaN values in data and both allow_nas and warning_nas are set to TRUE.

• An informative error message is thrown and the execution is stopped if:

  • data is not eligible to have been generated by a series of Bernoulli trials.

  • data is eligible to have been generated by a series of Bernoulli trials, there are some NA or NaN values in data and allow_nas is set to FALSE.

See Also

• inspect_par_bernoulli to validate Bernoulli/Binomial proportions.

• inspect_data_categorical and inspect_data_cat_as_dichotom to validate categorical data.

• inspect_par_multinomial to validate vectors of Multinomial proportions.

Examples

# Calls that pass silently:
x1 <- c(1, 0, 0, 1, 0)
x2 <- c(FALSE, FALSE, TRUE)
x3 <- c("yes", "no", "yes")
x4 <- factor(c("yes", "no", "yes"))
x5 <- c(1, 0, 0, 1, 0, NA)
inspect_data_dichotomous(x1, success = 1)
inspect_data_dichotomous(x2, success = TRUE)
inspect_data_dichotomous(x3, success = "yes")
inspect_data_dichotomous(x4, success = "yes")
inspect_data_dichotomous(x5, success = 1)

# Calls that throw an informative warning message:
y1 <- c(1, 1, NA, 0, 0)
y2 <- c(0, 0)
success <- 1
try(inspect_data_dichotomous(y1, success = 1, warning_nas = TRUE))
try(inspect_data_dichotomous(y2, success = success))

# Calls that throw an informative error message:
try(inspect_data_dichotomous(NULL, 1))
try(inspect_data_dichotomous(c(1, 0), NULL))
try(inspect_data_dichotomous(list(1, 0), 1))
try(inspect_data_dichotomous(c(1, 0), list(1)))
try(inspect_data_dichotomous(numeric(0), 0))
try(inspect_data_dichotomous(1, numeric(0)))
try(inspect_data_dichotomous(NaN, 1))
try(inspect_data_dichotomous(NA, 1))
try(inspect_data_dichotomous(c(1, 0), NA))
try(inspect_data_dichotomous(c(1, 0), NaN))
try(inspect_data_dichotomous(c(1, 0), 2))

Validate logarithmic bases

Description

inspect_log_base checks if an object is a valid a logarithmic base. This can be useful to validate inputs in user-defined functions.

Usage

inspect_log_base(x)

Arguments

Details

inspect_log_base conducts a series of tests to check if x is a valid logarithmic base. Namely, inspect_log_base checks if:

• x is NULL or empty.

• x is an atomic vector of length 1.

• x is numeric.

• x is NA or NaN.

• x is positive.

Value

inspect_log_base does not return any output. There are two possible outcomes:

• The call is silent if x is a numeric vector of length 1 that is a valid logarithmic base.

• An informative error message is thrown otherwise.

See Also

• bfactor_log_interpret for the interpretation of the logarithms of Bayes factors.

• inspect_bfactor_log to check if an object is a numeric vector of valid logarithmic Bayes factor values.

Examples

# Calls that pass silently:
x1 <- 10
x2 <- exp(1)
x3 <- 0.5
inspect_log_base(x1)
inspect_log_base(x2)
inspect_log_base(x3)

# Calls that throw informative error messages:
mylist <- list(
  NULL, numeric(0), TRUE, factor(10),
  list(10), matrix(10), NaN, NA, -1, 0
)
try(inspect_log_base(mylist[[1]]))
try(inspect_log_base(mylist[[2]]))
try(inspect_log_base(mylist[[3]]))
try(inspect_log_base(mylist[[4]]))
try(inspect_log_base(mylist[[5]]))
try(inspect_log_base(mylist[[6]]))
try(inspect_log_base(mylist[[7]]))
try(inspect_log_base(mylist[[8]]))
try(inspect_log_base(mylist[[9]]))
try(inspect_log_base(mylist[[10]]))

Validate parameters for the Bernoulli/Binomial distributions

Description

inspect_par_bernoulli checks if an object is an eligible Bernoulli/Binomial proportion. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_par_bernoulli(x)

Arguments

Details

inspect_par_bernoulli conducts a series of tests to check if x is an eligible Bernoulli/Binomial proportion. Namely, inspect_par_bernoulli checks if:

• x is NULL or empty.

• x is an atomic vector

• x is numeric

• x has length 1

• x is NA or NaN.

• x is in the (0, 1) interval.

Value

inspect_par_bernoulli does not return any output. There are two possible outcomes:

• The call is silent if x is an eligible Bernoulli/Binomial proportion.

• An informative error message is thrown otherwise.

See Also

• inspect_par_multinomial to validate parameters for the Multinomial distribution.

• inspect_par_beta to validate parameters for the Beta distribution.

• inspect_par_dirichlet to validate parameters for the Dirichlet distribution.

• inspect_par_haldane to validate parameters for the Haldane distribution.

• inspect_data_dichotomous to validate dichotomous data.

• inspect_prob to check if an object is a numeric vector of valid probability values.

Examples

# Calls that pass silently:
x <- 0.5
inspect_par_bernoulli(x)
inspect_par_bernoulli(0.1)

# Calls that throw an informative error message:
mylist <- list(
  NULL, TRUE, factor(.5), matrix(0.5), "0.5",
  list(0.5), NA, NaN, numeric(0), c(0.1, 0.5), -0.5, 1.1
)
try(inspect_par_bernoulli(mylist[[1]]))
try(inspect_par_bernoulli(mylist[[2]]))
try(inspect_par_bernoulli(mylist[[3]]))
try(inspect_par_bernoulli(mylist[[4]]))
try(inspect_par_bernoulli(mylist[[5]]))
try(inspect_par_bernoulli(mylist[[6]]))
try(inspect_par_bernoulli(mylist[[7]]))
try(inspect_par_bernoulli(mylist[[8]]))
try(inspect_par_bernoulli(mylist[[9]]))
try(inspect_par_bernoulli(mylist[[10]]))
try(inspect_par_bernoulli(mylist[[11]]))
try(inspect_par_bernoulli(mylist[[12]]))

Validate parameters for the Beta distribution

Description

inspect_par_beta checks if an object is an eligible vector of parameters for the Beta distribution. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_par_beta(x)

Arguments

Details

inspect_par_beta conducts a series of tests to check if x is an eligible vector of parameters for the Beta distribution. Namely, inspect_par_beta checks if:

• x is NULL or empty.

• x is an atomic vector

• x is numeric

• x has length 2

• x has NA or NaN values.

• All elements of x are positive.

Value

inspect_par_beta does not return any output. There are two possible outcomes:

• The call is silent if x is an eligible vector of parameters for the Beta distribution.

• An informative error message is thrown otherwise.

See Also

• inspect_par_bernoulli to validate parameters for the Bernoulli/Binomial distribution.

• inspect_par_multinomial to validate parameters for the Multinomial distribution.

• inspect_par_dirichlet to validate parameters for the Dirichlet distribution.

• inspect_par_haldane to validate parameters for the Haldane distribution.

Examples

# Calls that pass silently:
x1 <- c(1, 1)
x2 <- c(2, 5)
inspect_par_beta(x1)
inspect_par_beta(x2)

# Calls that throw an informative error message:
mylist <- list(
  NULL, 1, factor(1, 1),
  matrix(c(1, 1)), c("1", "1"), list(1, 1), c(1, NA),
  c(1, NaN), c(TRUE, FALSE), numeric(0), c(-1, 1)
)
try(inspect_par_beta(mylist[[1]]))
try(inspect_par_beta(mylist[[2]]))
try(inspect_par_beta(mylist[[3]]))
try(inspect_par_beta(mylist[[4]]))
try(inspect_par_beta(mylist[[5]]))
try(inspect_par_beta(mylist[[6]]))
try(inspect_par_beta(mylist[[7]]))
try(inspect_par_beta(mylist[[8]]))
try(inspect_par_beta(mylist[[9]]))
try(inspect_par_beta(mylist[[10]]))
try(inspect_par_beta(mylist[[11]]))

Validate parameters for the Dirichlet distribution

Description

inspect_par_dirichlet checks if an object is an eligible vector of parameters for the Dirichlet distribution. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_par_dirichlet(x)

Arguments

Details

inspect_par_dirichlet conducts a series of tests to check if x is an eligible vector of parameters for the Dirichlet distribution. Namely, inspect_par_dirichlet checks if:

• x is NULL or empty.

• x is an atomic vector

• x is numeric

• x has NA or NaN values.

• All elements of x are positive.

Value

inspect_par_dirichlet does not return any output. There are two possible outcomes:

• The call is silent if x is an eligible vector of parameters for the Dirichlet distribution.

• An informative error message is thrown otherwise.

See Also

• inspect_par_bernoulli to validate parameters for the Bernoulli/Binomial distribution.

• inspect_par_multinomial to validate parameters for the Multinomial distribution.

• inspect_par_beta to validate parameters for the Beta distribution.

• inspect_par_haldane to validate parameters for the Haldane distribution.

Examples

# Calls that pass silently:
x1 <- c(1, 1, 1)
x2 <- c(2, 5)
inspect_par_dirichlet(x1)
inspect_par_dirichlet(x2)

# Calls that throw an informative error message:
mylist <- list(
  NULL, factor(1, 1, 1),
  matrix(c(1, 1, 1)), c("1", "1", "1"), list(1, 1, 1), c(1, NA),
  c(1, NaN, 1), c(TRUE, FALSE), numeric(0), c(-1, 1, 1)
)
try(inspect_par_dirichlet(mylist[[1]]))
try(inspect_par_dirichlet(mylist[[2]]))
try(inspect_par_dirichlet(mylist[[3]]))
try(inspect_par_dirichlet(mylist[[4]]))
try(inspect_par_dirichlet(mylist[[5]]))
try(inspect_par_dirichlet(mylist[[6]]))
try(inspect_par_dirichlet(mylist[[7]]))
try(inspect_par_dirichlet(mylist[[8]]))
try(inspect_par_dirichlet(mylist[[9]]))
try(inspect_par_dirichlet(mylist[[10]]))

Validate parameters for the Haldane distribution

Description

inspect_par_haldane checks if an object is an eligible vector of parameters for the Haldane distribution. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_par_haldane(x)

Arguments

Details

inspect_par_haldane conducts a series of tests to check if x is an eligible vector of parameters for the Haldane distribution. Namely, inspect_par_haldane checks if:

• x is NULL or empty.

• x is an atomic vector

• x is numeric

• x has NA or NaN values.

• All elements of x equal to 0.

Value

inspect_par_haldane does not return any output. There are two possible outcomes:

• The call is silent if x is an eligible vector of parameters for the Haldane distribution.

• An informative error message is thrown otherwise.

See Also

• inspect_par_bernoulli to validate parameters for the Bernoulli/Binomial distribution.

• inspect_par_multinomial to validate parameters for the Multinomial distribution.

• inspect_par_beta to validate parameters for the Beta distribution.

• inspect_par_dirichlet to validate parameters for the Dirichlet distribution.

Examples

# Calls that pass silently:
x1 <- c(0, 0, 0)
x2 <- c(0, 0)
inspect_par_haldane(x1)
inspect_par_haldane(x2)

# Calls that throw an informative error message:
mylist <- list(
  NULL, factor(0, 0, 0),
  matrix(c(0, 0, 0)), c("0", "0", "0"), list(0, 0, 0), c(0, NA),
  c(0, NaN, 0), c(TRUE, FALSE), numeric(0), c(1, 0, 0)
)
try(inspect_par_haldane(mylist[[1]]))
try(inspect_par_haldane(mylist[[2]]))
try(inspect_par_haldane(mylist[[3]]))
try(inspect_par_haldane(mylist[[4]]))
try(inspect_par_haldane(mylist[[5]]))
try(inspect_par_haldane(mylist[[6]]))
try(inspect_par_haldane(mylist[[7]]))
try(inspect_par_haldane(mylist[[8]]))
try(inspect_par_haldane(mylist[[9]]))
try(inspect_par_haldane(mylist[[10]]))

Validate parameters for the Multinomial distribution

Description

inspect_par_multinomial checks if an object is an eligible vector of Multinomial proportions. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_par_multinomial(x)

Arguments

Details

inspect_par_multinomial conducts a series of tests to check if x is an eligible vector of Multinomial proportions. Namely, inspect_par_multinomial checks if:

• x is NULL or empty.

• x is an atomic vector

• x is numeric

• x has NA or NaN values.

• All elements of x are in the (0, 1) interval.

• x sums to 1.

Value

inspect_par_multinomial does not return any output. There are two possible outcomes:

• The call is silent if x is an eligible vector of Multinomial proportions.

• An informative error message is thrown otherwise.

See Also

• inspect_par_bernoulli to validate parameters for the Bernoulli/Binomial distribution.

• inspect_par_beta to validate parameters for the Beta distribution.

• inspect_par_dirichlet to validate parameters for the Dirichlet distribution.

• inspect_par_haldane to validate parameters for the Haldane distribution.

• inspect_data_categorical and inspect_data_cat_as_dichotom to validate categorical data.

• inspect_prob to check if an object is a numeric vector of valid probability values.

Examples

# Calls that pass silently:
x1 <- c(0.5, 0.5)
x2 <- rep(1 / 5, 5)
inspect_par_multinomial(x1)
inspect_par_multinomial(x2)

# Calls that throw an informative error message:
mylist <- list(
  NULL, TRUE, factor(0.5, 0.5),
  matrix(c(0.5, 0.5)), c("0.5", "0.5"), list(0.5, 0.5),
  c(0.9, NA), c(0.9, NaN), numeric(0), NA, c(0.9, 0.6), c(-0.1, 0.9)
)
try(inspect_par_multinomial(mylist[[1]]))
try(inspect_par_multinomial(mylist[[2]]))
try(inspect_par_multinomial(mylist[[3]]))
try(inspect_par_multinomial(mylist[[4]]))
try(inspect_par_multinomial(mylist[[5]]))
try(inspect_par_multinomial(mylist[[6]]))
try(inspect_par_multinomial(mylist[[7]]))
try(inspect_par_multinomial(mylist[[8]]))
try(inspect_par_multinomial(mylist[[9]]))
try(inspect_par_multinomial(mylist[[10]]))
try(inspect_par_multinomial(mylist[[11]]))
try(inspect_par_multinomial(mylist[[12]]))

Validate vectors of probabilities

Description

inspect_prob checks if an object is a numeric vector of valid probability values. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

Usage

inspect_prob(x, allow_nas = TRUE, warning_nas = TRUE)

Arguments

Details

inspect_prob conducts a series of tests to check if x is a numeric vector of valid probability values. Namely, inspect_prob checks if:

• x is NULL or empty.

• x is an atomic vector.

• x is numeric.

• x has NA or NaN values.

• The values of x are in the [0, 1] interval.

Value

inspect_prob does not return any output. There are three possible outcomes:

• The call is silent if:

  • x is a numeric vector of valid probability values and there are no NA or NaN values in x.

  • x is a numeric vector of valid probability values, there are some NA or NaN values in x, allow_nas is set to TRUE and warning_nas is set to FALSE.

• An informative warning message is thrown if x is a numeric vector of valid probability values, there are some NA or NaN values in x and both allow_nas and warning_nas are set to TRUE.

• An informative error message is thrown and the execution is stopped if:

  • x is not a numeric vector of valid probability values.

  • x is a numeric vector of valid probability values, there are some NA or NaN values in x and allow_nas is set to FALSE.

See Also

• inspect_par_bernoulli to check if an object is a valid Bernoulli/Binomial proportion.

• inspect_par_multinomial to check if an object is a numeric vector of valid Multinomial proportions.

Examples

# Calls that pass silently:
x1 <- c(0.1, 0.2, 0.3, 0.4, 0.5)
x2 <- c(0.1, 0.2, 0.3, 0.4, 0.5, NA)
inspect_prob(x1)
inspect_prob(x2, warning_nas = FALSE)
inspect_prob(x2, allow_nas = TRUE, warning_nas = FALSE)

# Calls that throw an informative warning message:
y <- c(0.1, 0.2, NA, 0.4, 0.5)
try(inspect_prob(y))
try(inspect_prob(y, allow_nas = TRUE))
try(inspect_prob(y, allow_nas = TRUE, warning_nas = TRUE))

# Calls that throw an informative error message:
z1 <- c(-0.9, 0, 0.1, 0.2, 0.3, 0.4, 0.5)
try(inspect_prob(z1))
z2 <- c(NA, 0, 0.1, 0.2, 0.3, 0.4, 0.5)
try(inspect_prob(z2, allow_nas = FALSE))
mylist <- list(
  NULL, TRUE, factor(.5), matrix(0.5),
  "0.5", list(0.5), NA, NaN, numeric(0), 1.1, -0.5
)
try(inspect_prob(mylist[[1]]))
try(inspect_prob(mylist[[2]]))
try(inspect_prob(mylist[[3]]))
try(inspect_prob(mylist[[4]]))
try(inspect_prob(mylist[[5]]))
try(inspect_prob(mylist[[6]]))
try(inspect_prob(mylist[[7]]))
try(inspect_prob(mylist[[8]]))
try(inspect_prob(mylist[[9]]))
try(inspect_prob(mylist[[10]]))
try(inspect_prob(mylist[[11]]))

Validate non-missing logical values

Description

inspect_true_or_false checks if an object is a non-missing logical vector of length 1. This can be useful to validate inputs in user-defined functions.

Usage

inspect_true_or_false(x)

Arguments

Details

inspect_true_or_false conducts a series of tests to check if x is a non-missing logical vector of length 1. Namely, inspect_true_or_false checks if:

• x is NULL or empty.

• x is an atomic vector of length 1.

• The typeof x is logical.

• x is NA or NaN.

Value

inspect_true_or_false does not return any output. There are two possible scenarios:

• The call is silent if x is a non-missing logical vector of length 1.

• An informative error message is thrown otherwise.

See Also

• inspect_character to validate character vectors.

• inspect_character_match to validate character vectors with predefined allowed values.

Examples

# Calls that pass silently:
x <- TRUE
y <- FALSE
inspect_true_or_false(x)
inspect_true_or_false(y)

# Calls that throw informative error messages:
mylist <- list(NULL, NA, NaN, 1, 0, "TRUE")
try(inspect_true_or_false(mylist[[1]]))
try(inspect_true_or_false(mylist[[2]]))
try(inspect_true_or_false(mylist[[3]]))
try(inspect_true_or_false(mylist[[4]]))
try(inspect_true_or_false(mylist[[5]]))
try(inspect_true_or_false(mylist[[6]]))