Title:	Network Analysis of Dependencies of CRAN Packages
Version:	0.3.13
Description:	The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <doi:10.1111/stan.12355>, of which functions are provided.
Depends:	R (≥ 3.4)
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/clement-lee/crandep
BugReports:	https://github.com/clement-lee/crandep/issues
Encoding:	UTF-8
LazyData:	true
Imports:	stringr, dplyr, igraph, Rcpp, pracma, gsl, utils, tools, stats
Suggests:	ggplot2, tibble, visNetwork, knitr, rmarkdown
RoxygenNote:	7.2.3
NeedsCompilation:	yes
SystemRequirements:	pandoc (>= 1.12.3) - http://pandoc.org
Packaged:	2025-06-16 11:03:03 UTC; ntl34
Author:	Clement Lee [aut, cre]
Maintainer:	Clement Lee <clement.lee.tm@outlook.com>
VignetteBuilder:	knitr
LinkingTo:	Rcpp, RcppArmadillo
Repository:	CRAN
Date/Publication:	2025-06-16 13:10:11 UTC

Survival function of 2-component discrete extreme value mixture distribution

Description

Smix2 returns the survival function at x for the 2-component discrete extreme value mixture distribution. The components below and above the threshold u are the (truncated) Zipf-polylog(alpha,theta) and the generalised Pareto(shape, sigma) distributions, respectively.

Usage

Smix2(x, u, alpha, theta, shape, sigma, phiu)

Arguments

Value

A numeric vector of the same length as x

See Also

dmix2 for the corresponding probability mass function, Spol and Smix3 for the survival functions of the Zipf-polylog and 3-component discrete extreme value mixture distributions, respectively.

Survival function of 3-component discrete extreme value mixture distribution

Description

Smix3 returns the survival function at x for the 3-component discrete extreme value mixture distribution. The component below v is the (truncated) Zipf-polylog(alpha1,theta1) distribution, between v & u the (truncated) Zipf-polylog(alpha2,theta2) distribution, and above u the generalised Pareto(shape, sigma) distribution.

Usage

Smix3(x, v, u, alpha1, theta1, alpha2, theta2, shape, sigma, phi1, phi2, phiu)

Arguments

Value

A numeric vector of the same length as x

See Also

dmix3 for the corresponding probability mass function, Spol and Smix2 for the survival functions of the Zipf-polylog and 2-component discrete extreme value mixture distributions, respectively.

Survival function of Zipf-polylog distribution

Description

Spol returns the survival function at x for the Zipf-polylog distribution with parameters (alpha, theta). The distribution is reduced to the discrete power law when theta = 1.

Usage

Spol(x, alpha, theta, x_max = 100000L)

Arguments

Value

A numeric vector of the same length as x

See Also

dpol for the corresponding probability mass function, Smix2 and Smix3 for the survival functions of the 2-component and 3-component discrete extreme value mixture distributions, respectively.

Examples

Spol(c(1,2,3,4,5), 1.2, 0.5)

Check and convert dependency word(s)

Description

Check and convert dependency word(s)

Usage

check_dep_word(x)

Arguments

Value

A character vector of modified dependency words

Citation network of CHI papers

Description

A dataset containing the citations of conference papers of the ACM Conference on Human Factors in Computing Systems (CHI) from 1981 to 2019, obtained from the ACM digital library. The resulting citation network can be compared to the dependencies network of CRAN packages, in terms of network-related characteristics, such as degree distribution and sparsity.

Usage

chi_citations

Format

A data from with 21951 rows and 4 variables:

from

the unique identifier (in the digital library) of the paper that cites other papers

to

the unique identifier of the paper that is being cited

year_from

the publication year of the citing paper

year_to

the publication year of the cited paper

Source

https://dl.acm.org/conference/chi

See Also

cran_dependencies

Conditionally change a string

Description

Conditionally change a string

Usage

conditional_change(x, from, to)

Arguments

Value

A string

Dependencies of CRAN packages

Description

A dataset containing the dependencies of various types (Imports, Depends, Suggests, LinkingTo, and their reverse counterparts) of more than 14600 packages available on CRAN as of 2020-05-09.

Usage

cran_dependencies

Format

A data frame with 211408 rows and 4 variables:

from

the name of the package that introduced the dependencies

to

the name of the package that the dependency is directed towards

type

the type of dependency, which can take the follow values (all in lowercase): "depends", "imports", "linking to", "suggests"

reverse

a boolean representing whether the dependency is a reverse one (TRUE) or a forward one (FALSE)

Source

The CRAN pages of all the packages available on https://cran.r-project.org

See Also

chi_citations

Construct the giant component of the network from two data frames

Description

Construct the giant component of the network from two data frames

Usage

df_to_graph(edgelist, nodelist = NULL, gc = TRUE)

Arguments

Value

An igraph object & a connected graph if gc is 'TRUE'

Examples

from <- c("1", "2", "4")
to <- c("2", "3", "5")
edges <- data.frame(from = from, to = to, stringsAsFactors = FALSE)
nodes <- data.frame(name = c("1", "2", "3", "4", "5"), stringsAsFactors = FALSE)
df_to_graph(edges, nodes)

Probability mass function (PMF) of 2-component discrete extreme value mixture distribution

Description

dmix2 returns the PMF at x for the 2-component discrete extreme value mixture distribution. The components below and above the threshold u are the (truncated) Zipf-polylog(alpha,theta) and the generalised Pareto(shape, sigma) distributions, respectively.

Usage

dmix2(x, u, alpha, theta, shape, sigma, phiu)

Arguments

Value

A numeric vector of the same length as x

See Also

Smix2 for the corresponding survival function, dpol and dmix3 for the PMFs of the Zipf-polylog and 3-component discrete extreme value mixture distributions, respectively.

Probability mass function (PMF) of 3-component discrete extreme value mixture distribution

Description

dmix3 returns the PMF at x for the 3-component discrete extreme value mixture distribution. The component below v is the (truncated) Zipf-polylog(alpha1,theta1) distribution, between v & u the (truncated) Zipf-polylog(alpha2,theta2) distribution, and above u the generalised Pareto(shape, sigma) distribution.

Usage

dmix3(x, v, u, alpha1, theta1, alpha2, theta2, shape, sigma, phi1, phi2, phiu)

Arguments

Value

A numeric vector of the same length as x

See Also

Smix3 for the corresponding survival function, dpol and dmix2 for the PMFs of the Zipf-polylog and 2-component discrete extreme value mixture distributions, respectively.

Probability mass function (PMF) of Zipf-polylog distribution

Description

dpol returns the PMF at x for the Zipf-polylog distribution with parameters (alpha, theta). The distribution is reduced to the discrete power law when theta = 1.

Usage

dpol(x, alpha, theta, x_max = 100000L)

Arguments

Details

The PMF is proportional to x^(-alpha) * theta^x. It is normalised in order to be a proper PMF.

Value

A numeric vector of the same length as x

See Also

Spol for the corresponding survival function, dmix2 and dmix3 for the PMFs of the 2-component and 3-component discrete extreme value mixture distributions, respectively.

Examples

dpol(c(1,2,3,4,5), 1.2, 0.5)

Multiple types of dependencies

Description

get_dep returns a data frame of multiple types of dependencies of a package

Usage

get_dep(name, type, reverse = FALSE)

Arguments

Value

A data frame of dependencies

See Also

get_dep_all_packages for the dependencies of all CRAN packages, and get_graph_all_packages for obtaining directly a network of dependencies as an igraph object

Examples

get_dep("dplyr", c("Imports", "Depends"))
get_dep("MASS", c("Suggests", "Depends", "Imports"), TRUE)

Dependencies of all CRAN packages

Description

get_dep_all_packages returns the data frame of dependencies of all packages currently available on CRAN.

Usage

get_dep_all_packages()

Value

A list of two data frames, one the names of all CRAN packages, the other their dependencies

See Also

get_dep for multiple types of dependencies, and get_graph_all_packages for obtaining directly a network of dependencies as an igraph object

Examples

## Not run: 
df.cran <- get_dep_all_packages()

## End(Not run)

Split a string to a list of dependencies

Description

Split a string to a list of dependencies

Usage

get_dep_vec(x)

Arguments

Value

A string vector of dependencies

Graph of dependencies of all CRAN packages

Description

get_graph_all_packages returns an igraph object representing the network of one or more types of dependencies of all CRAN packages.

Usage

get_graph_all_packages(type, gc = TRUE, reverse = FALSE)

Arguments

Value

An igraph object & a connected graph if gc is 'TRUE'

See Also

get_dep_all_packages for the dependencies of all CRAN packages in a data frame, and df_to_graph for constructing the giant component of the network from two data frames

Examples

## Not run: 
g0.cran.depends <- get_graph_all_packages("depends")
g1.cran.imports <- get_graph_all_packages("imports", reverse = TRUE)

## End(Not run)

Wrapper of lpost_bulk, assuming power law (theta = 1.0)

Description

Wrapper of lpost_bulk, assuming power law (theta = 1.0)

Usage

lpost_bulk_wrapper(alpha, ...)

Arguments

Value

A scalar of the log-posterior density

Wrapper of lpost_mix2, assuming power law (theta = 1.0) & contrained (alpha > 1.0, xi < 1.0 / (alpha - 1.0))

Description

Wrapper of lpost_mix2, assuming power law (theta = 1.0) & contrained (alpha > 1.0, xi < 1.0 / (alpha - 1.0))

Usage

lpost_mix2_constrained(par, ...)

Arguments

Value

A scalar of the log-posterior density

Wrapper of lpost_pol, assuming power law (theta = 1.0)

Description

Wrapper of lpost_pol, assuming power law (theta = 1.0)

Usage

lpost_pol_wrapper(alpha, x, count, ...)

Arguments

Value

A scalar of the log-posterior density

Unnormalised log-posterior density of discrete power law

Description

Unnormalised log-posterior density of discrete power law

Usage

lpost_pow(alpha, df, m_alpha, s_alpha)

Arguments

Value

A real number

Marginal log-likelihood and posterior density of discrete power law via numerical integration

Description

Marginal log-likelihood and posterior density of discrete power law via numerical integration

Usage

marg_pow(df, lower, upper, m_alpha = 0, s_alpha = 10, by = 0.001)

Arguments

Value

A list: log_marginal is the marginal log-likelihood, posterior is a data frame of non-zero posterior densities

Markov chain Monte Carlo for TZP-power-law mixture

Description

mcmc_mix1 returns the posterior samples of the parameters, for fitting the TZP-power-law mixture distribution. The samples are obtained using Markov chain Monte Carlo (MCMC).

Usage

mcmc_mix1(
  x,
  count,
  u_set,
  u,
  alpha1,
  theta1,
  alpha2,
  a_psiu,
  b_psiu,
  a_alpha1,
  b_alpha1,
  a_theta1,
  b_theta1,
  a_alpha2,
  b_alpha2,
  positive,
  iter,
  thin,
  burn,
  freq,
  invt,
  mc3_or_marg,
  x_max
)

Arguments

Details

In the MCMC, a componentwise Metropolis-Hastings algorithm is used. The threshold u is treated as a parameter and therefore sampled. The hyperparameters are used in the following priors: u is such that the implied unique exceedance probability psiu ~ Uniform(a_psi, b_psi); alpha1 ~ Normal(mean = a_alpha1, sd = b_alpha1); theta1 ~ Beta(a_theta1, b_theta1); alpha2 ~ Normal(mean = a_alpha2, sd = b_alpha2)

Value

A list: $pars is a data frame of iter rows of the MCMC samples, $fitted is a data frame of length(x) rows with the fitted values, amongst other quantities related to the MCMC

See Also

mcmc_pol, mcmc_mix2 and mcmc_mix3 for MCMC for the Zipf-polylog, and 2-component and 3-component discrete extreme value mixture distributions, respectively.

Wrapper of mcmc_mix1

Description

Wrapper of mcmc_mix1

Usage

mcmc_mix1_wrapper(
  df,
  seed,
  u_max = 2000L,
  log_diff_max = 11,
  a_psiu = 0.1,
  b_psiu = 0.9,
  m_alpha1 = 0,
  s_alpha1 = 10,
  a_theta1 = 1,
  b_theta1 = 1,
  m_alpha2 = 0,
  s_alpha2 = 10,
  positive = FALSE,
  iter = 20000L,
  thin = 1L,
  burn = 10000L,
  freq = 100L,
  invts = 1,
  mc3_or_marg = TRUE,
  x_max = 1e+05
)

Arguments

Value

A list returned by mcmc_mix1

Markov chain Monte Carlo for 2-component discrete extreme value mixture distribution

Description

mcmc_mix2 returns the posterior samples of the parameters, for fitting the 2-component discrete extreme value mixture distribution. The samples are obtained using Markov chain Monte Carlo (MCMC).

Usage

mcmc_mix2(
  x,
  count,
  u_set,
  u,
  alpha,
  theta,
  shape,
  sigma,
  a_psiu,
  b_psiu,
  a_alpha,
  b_alpha,
  a_theta,
  b_theta,
  m_shape,
  s_shape,
  a_sigma,
  b_sigma,
  positive,
  a_pseudo,
  b_pseudo,
  pr_power,
  iter,
  thin,
  burn,
  freq,
  invt,
  mc3_or_marg = TRUE,
  constrained = FALSE
)

Arguments

Details

In the MCMC, a componentwise Metropolis-Hastings algorithm is used. The threshold u is treated as a parameter and therefore sampled. The hyperparameters are used in the following priors: u is such that the implied unique exceedance probability psiu ~ Uniform(a_psi, b_psi); alpha ~ Normal(mean = a_alpha, sd = b_alpha); theta ~ Beta(a_theta, b_theta); shape ~ Normal(mean = m_shape, sd = s_shape); sigma ~ Gamma(a_sigma, scale = b_sigma). If pr_power = 1.0, the discrete power law (below u) is assumed, and the samples of theta will be all 1.0. If pr_power is in (0.0, 1.0), model selection between the polylog distribution and the discrete power law will be performed within the MCMC.

Value

A list: $pars is a data frame of iter rows of the MCMC samples, $fitted is a data frame of length(x) rows with the fitted values, amongst other quantities related to the MCMC

See Also

mcmc_pol and mcmc_mix3 for MCMC for the Zipf-polylog and 3-component discrete extreme value mixture distributions, respectively.

Wrapper of mcmc_mix2

Description

Wrapper of mcmc_mix2

Usage

mcmc_mix2_wrapper(
  df,
  seed,
  u_max = 2000L,
  log_diff_max = 11,
  a_psiu = 0.001,
  b_psiu = 0.9,
  m_alpha = 0,
  s_alpha = 10,
  a_theta = 1,
  b_theta = 1,
  m_shape = 0,
  s_shape = 10,
  a_sigma = 1,
  b_sigma = 0.01,
  a_pseudo = 10,
  b_pseudo = 1,
  pr_power = 0.5,
  positive = FALSE,
  iter = 20000L,
  thin = 20L,
  burn = 100000L,
  freq = 1000L,
  invts = 1,
  mc3_or_marg = TRUE,
  constrained = FALSE
)

Arguments

Value

A list returned by mcmc_mix2

Markov chain Monte Carlo for 3-component discrete extreme value mixture distribution

Description

mcmc_mix3 returns the posterior samples of the parameters, for fitting the 3-component discrete extreme value mixture distribution. The samples are obtained using Markov chain Monte Carlo (MCMC).

Usage

mcmc_mix3(
  x,
  count,
  v_set,
  u_set,
  v,
  u,
  alpha1,
  theta1,
  alpha2,
  theta2,
  shape,
  sigma,
  a_psi1,
  a_psi2,
  a_psiu,
  b_psiu,
  a_alpha1,
  b_alpha1,
  a_theta1,
  b_theta1,
  a_alpha2,
  b_alpha2,
  a_theta2,
  b_theta2,
  m_shape,
  s_shape,
  a_sigma,
  b_sigma,
  powerlaw1,
  positive1,
  positive2,
  a_pseudo,
  b_pseudo,
  pr_power2,
  iter,
  thin,
  burn,
  freq,
  invt,
  mc3_or_marg = TRUE
)

Arguments

Details

In the MCMC, a componentwise Metropolis-Hastings algorithm is used. The thresholds v and u are treated as parameters and therefore sampled. The hyperparameters are used in the following priors: psi1 / (1.0 - psiu) ~ Beta(a_psi1, a_psi2); u is such that the implied unique exceedance probability psiu ~ Uniform(a_psi, b_psi); alpha1 ~ Normal(mean = a_alpha1, sd = b_alpha1); theta1 ~ Beta(a_theta1, b_theta1); alpha2 ~ Normal(mean = a_alpha2, sd = b_alpha2); theta2 ~ Beta(a_theta2, b_theta2); shape ~ Normal(mean = m_shape, sd = s_shape); sigma ~ Gamma(a_sigma, scale = b_sigma). If pr_power2 = 1.0, the discrete power law (between v and u) is assumed, and the samples of theta2 will be all 1.0. If pr_power2 is in (0.0, 1.0), model selection between the polylog distribution and the discrete power law will be performed within the MCMC.

Value

A list: $pars is a data frame of iter rows of the MCMC samples, $fitted is a data frame of length(x) rows with the fitted values, amongst other quantities related to the MCMC

See Also

mcmc_pol and mcmc_mix2 for MCMC for the Zipf-polylog and 2-component discrete extreme value mixture distributions, respectively.

Wrapper of mcmc_mix3

Description

Wrapper of mcmc_mix3

Usage

mcmc_mix3_wrapper(
  df,
  seed,
  v_max = 100L,
  u_max = 2000L,
  log_diff_max = 11,
  a_psi1 = 1,
  a_psi2 = 1,
  a_psiu = 0.001,
  b_psiu = 0.9,
  m_alpha = 0,
  s_alpha = 10,
  a_theta = 1,
  b_theta = 1,
  m_shape = 0,
  s_shape = 10,
  a_sigma = 1,
  b_sigma = 0.01,
  a_pseudo = 10,
  b_pseudo = 1,
  pr_power2 = 0.5,
  powerlaw1 = FALSE,
  positive1 = FALSE,
  positive2 = TRUE,
  iter = 20000L,
  thin = 20L,
  burn = 100000L,
  freq = 1000L,
  invts = 1,
  mc3_or_marg = TRUE
)

Arguments

Value

A list returned by mcmc_mix3

Markov chain Monte Carlo for Zipf-polylog distribution

Description

mcmc_pol returns the samples from the posterior of alpha and theta, for fitting the Zipf-polylog distribution to the data x. The samples are obtained using Markov chain Monte Carlo (MCMC). In the MCMC, a Metropolis-Hastings algorithm is used.

Usage

mcmc_pol(
  x,
  count,
  alpha,
  theta,
  a_alpha,
  b_alpha,
  a_theta,
  b_theta,
  a_pseudo,
  b_pseudo,
  pr_power,
  iter,
  thin,
  burn,
  freq,
  invt,
  mc3_or_marg,
  x_max
)

Arguments

Value

A list: $pars is a data frame of iter rows of the MCMC samples, $fitted is a data frame of length(x) rows with the fitted values, amongst other quantities related to the MCMC

See Also

mcmc_mix2 and mcmc_mix3 for MCMC for the 2-component and 3-component discrete extreme value mixture distributions, respectively.

Wrapper of mcmc_pol

Description

Wrapper of mcmc_pol

Usage

mcmc_pol_wrapper(
  df,
  seed,
  alpha_init = 1.5,
  theta_init = 0.5,
  m_alpha = 0,
  s_alpha = 10,
  a_theta = 1,
  b_theta = 1,
  a_pseudo = 10,
  b_pseudo = 1,
  pr_power = 0.5,
  iter = 20000L,
  thin = 20L,
  burn = 100000L,
  freq = 1000L,
  invts = 1,
  mc3_or_marg = TRUE,
  x_max = 1e+05
)

Arguments

Value

A list returned by mcmc_pol

Obtain set of thresholds with high posterior density for the TZP-power-law mixture model

Description

obtain_u_set_mix1 computes the profile posterior density of the threshold u, and subsets the thresholds (and other parameter values) with high profile values i.e. within a certain value from the maximum posterior density. The set of u can then be used for mcmc_mix1.

Usage

obtain_u_set_mix1(
  df,
  positive = FALSE,
  u_max = 2000L,
  log_diff_max = 11,
  alpha1_init = 0.01,
  theta1_init = exp(-1),
  alpha2_init = 2,
  a_psiu = 0.1,
  b_psiu = 0.9,
  m_alpha1 = 0,
  s_alpha1 = 10,
  a_theta1 = 1,
  b_theta1 = 1,
  m_alpha2 = 0,
  s_alpha2 = 10,
  x_max = 1e+05
)

Arguments

Value

A list: u_set is the vector of thresholds with high posterior density, init is the data frame with the maximum profile posterior density and associated parameter values, profile is the data frame with all thresholds with high posterior density and associated parameter values, scalars is the data frame with all arguments (except df)

See Also

mcmc_mix1_wrapper that wraps obtain_u_set_mix1 and mcmc_mix1, obtain_u_set_mix2 for the equivalent function for the 2-component mixture model

Obtain set of thresholds with high posterior density for the 2-component mixture model

Description

obtain_u_set_mix2 computes the profile posterior density of the threshold u, and subsets the thresholds (and other parameter values) with high profile values i.e. within a certain value from the maximum posterior density. The set of u can then be used for mcmc_mix2.

Usage

obtain_u_set_mix2(
  df,
  powerlaw = FALSE,
  positive = FALSE,
  u_max = 2000L,
  log_diff_max = 11,
  alpha_init = 0.01,
  theta_init = exp(-1),
  shape_init = 0.1,
  sigma_init = 1,
  a_psiu = 0.001,
  b_psiu = 0.9,
  m_alpha = 0,
  s_alpha = 10,
  a_theta = 1,
  b_theta = 1,
  m_shape = 0,
  s_shape = 10,
  a_sigma = 1,
  b_sigma = 0.01
)

Arguments

Value

A list: u_set is the vector of thresholds with high posterior density, init is the data frame with the maximum profile posterior density and associated parameter values, profile is the data frame with all thresholds with high posterior density and associated parameter values, scalars is the data frame with all arguments (except df)

See Also

mcmc_mix2_wrapper that wraps obtain_u_set_mix2 and mcmc_mix2, obtain_u_set_mix1 for the equivalent function for the TZP-power-law mixture model

Obtain set of thresholds with high posterior density for the constrained 2-component mixture model

Description

obtain_u_set_mix2_constrained computes the profile posterior density of the threshold u, and subsets the thresholds (and other parameter values) with high profile values i.e. within a certain value from the maximum posterior density. The set of u can then be used for mcmc_mix2. Power law is assumed for the body, and alpha is assumed to be greater than 1.0 and smaller than 1.0/shape+1.0

Usage

obtain_u_set_mix2_constrained(
  df,
  u_max = 2000L,
  log_diff_max = 11,
  alpha_init = 2,
  shape_init = 0.1,
  sigma_init = 1,
  a_psiu = 0.001,
  b_psiu = 0.9,
  m_alpha = 0,
  s_alpha = 10,
  a_theta = 1,
  b_theta = 1,
  m_shape = 0,
  s_shape = 10,
  a_sigma = 1,
  b_sigma = 0.01
)

Arguments

Value

A list: u_set is the vector of thresholds with high posterior density, init is the data frame with the maximum profile posterior density and associated parameter values, profile is the data frame with all thresholds with high posterior density and associated parameter values, scalars is the data frame with all arguments (except df)

See Also

obtain_u_set_mix2 for the unconstrained version

Obtain set of thresholds with high posterior density for the 3-component mixture model

Description

obtain_u_set_mix3 computes the profile posterior density of the thresholds v & u, and subsets the thresholds (and other parameter values) with high profile values i.e. within a certain value from the maximum posterior density. The sets of v & u can then be used for mcmc_mix3.

Usage

obtain_u_set_mix3(
  df,
  powerlaw1 = FALSE,
  powerlaw2 = FALSE,
  positive1 = FALSE,
  positive2 = TRUE,
  log_diff_max = 11,
  v_max = 100L,
  u_max = 2000L,
  alpha_init = 0.01,
  theta_init = exp(-1),
  shape_init = 1,
  sigma_init = 1,
  a_psi1 = 1,
  a_psi2 = 1,
  a_psiu = 0.001,
  b_psiu = 0.9,
  m_alpha = 0,
  s_alpha = 10,
  a_theta = 1,
  b_theta = 1,
  m_shape = 0,
  s_shape = 10,
  a_sigma = 1,
  b_sigma = 0.01
)

Arguments

Value

A list: v_set is the vector of lower thresholds with high posterior density, u_set is the vector of upper thresholds with high posterior density, init is the data frame with the maximum profile posterior density and associated parameter values, profile is the data frame with all thresholds with high posterior density and associated parameter values, scalars is the data frame with all arguments (except df)

See Also

mcmc_mix3_wrapper that wraps obtain_u_set_mix3 and mcmc_mix3

Reshape the data frame of dependencies

Description

Reshape the data frame of dependencies

Usage

reshape_dep(x, names)

Arguments

Value

A data frame of dependencies