Package 'BCD' reference manual

Title:	Bivariate Distributions via Conditional Specification
Description:	Implementation of bivariate binomial, geometric, and Poisson distributions based on conditional specifications. The package also includes tools for data generation and goodness-of-fit testing for these three distribution families. For methodological details, see Ghosh, Marques, and Chakraborty (2025) <doi:10.1080/03610926.2024.2315294>, Ghosh, Marques, and Chakraborty (2023) <doi:10.1080/03610918.2021.2004419>, and Ghosh, Marques, and Chakraborty (2021) <doi:10.1080/02664763.2020.1793307>.
Authors:	Mina Norouzirad [aut, cre] (ORCID: <https://orcid.org/0000-0003-0311-6888>), Filipe J. Marques [aut] (ORCID: <https://orcid.org/0000-0001-6453-6558>), Indranil Ghosh [ctb] (ORCID: <https://orcid.org/0000-0001-7910-8919>), FCT, I.P. [fnd] (under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020 (NOVAMath))
Maintainer:	Mina Norouzirad <[email protected]>
License:	GPL (>= 2)
Version:	0.1.1
Built:	2026-05-26 05:57:22 UTC
Source:	https://github.com/mnrzrad/bcd

Aborted Flight Counts for 109 Aircrafts

Description

This dataset records the number of aborted flights by 109 aircrafts during two consecutive periods. The counts are cross-tabulated by the number of aborted flights in each period.

Usage

abortflights
abortflights

Format

A data frame with 109 rows and 2 variables:

X: Number of aborted flights in Period 1.
Y: Number of aborted flights in Period 2.

References

Barbiero, A. (2019). A bivariate geometric distribution allowing for positive or negative correlation. Communications in Statistics - Theory and Methods, 48 (11), 2842–-2861. doi:10.1080/03610926.2018.1473428.

Ghosh, I., Marques, F., & Chakraborty, S. (2023) A bivariate geometric distribution via conditional specification: properties and applications, Communications in Statistics - Simulation and Computation, 52:12, 5925–5945, doi:10.1080/03610918.2021.2004419

Examples

data(abortflights)
head(abortflights)
table(abortflights$X, abortflights$Y)
data(abortflights)
head(abortflights)
table(abortflights$X, abortflights$Y)

Joint Probability Mass Function for a Bivariate Binomial Distribution via Conditional Specification

Description

Computes the probability mass function (p.m.f.) of the bivariate binomial conditionals distribution (BBCD) as defined by Ghosh, Marques, and Chakraborty (2025). The distribution is characterized by conditional binomial distributions for $X$ and $Y$ .

Usage

dbinomBCD(x, y, n1, n2, p1, p2, lambda)
dbinomBCD(x, y, n1, n2, p1, p2, lambda)

Arguments

x

value of $X$ , must be in $\{0, 1, ..., n_1\}$

y

value of $Y$ , must be in $\{0, 1, ..., n_2\}$

n1

number of trials for $X$ , must be non-negative

n2

number of trials for $Y$ , must be non-negative

p1

base success probability for $X$ , in $(0, 1)$

p2

base success probability for $Y$ , in $(0, 1)$

lambda

dependence parameter, must be positive.

Details

The joint p.m.f. of the BBCD is

$P(X = x, Y = y) = K_B(n_1, n_2, p_1, p_2, \lambda) \binom{n_1}{x} \binom{n_2}{y} p_1^x p_2^y (1 - p_1)^{n_1 - x} (1 - p_2)^{n_2 - y} \lambda^{xy},$

where $x = 0, 1, \ldots, n_1$ , $y = 0, 1, \ldots, n_2$ , and $K_B(n_1, n_2, p_1, p_2, \lambda)$ is the normalizing constant.

Value

The probability $P(X = x, Y = y)$ .

References

Ghosh, I., Marques, F., & Chakraborty, S. (2025). A form of bivariate binomial conditionals distributions. Communications in Statistics - Theory and Methods, 54(2), 534–553. doi:10.1080/03610926.2024.2315294

Examples

# Compute P(X = 2, Y = 1) with n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5
dbinomBCD(x = 2, y = 1, n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5)

# Example with independence (lambda = 1)
dbinomBCD(x = 2, y = 1, n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 1.0)

# Compute P(X = 2, Y = 1) with n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5
dbinomBCD(x = 2, y = 1, n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5)

# Example with independence (lambda = 1)
dbinomBCD(x = 2, y = 1, n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 1.0)

Joint Probability Mass Function for A Bivariate Geometric Distribution via Conditional Specification

Description

Computes the joint probability mass function (p.m.f.) of a Bivariate Geometric Conditional Distributions (BGCD) based on Ghosh, Marques, and Chakraborty (2023). This distribution models paired count data with geometric conditionals, incorporating dependence between variables $X$ and $Y$ .

Usage

dgeomBCD(x, y, q1, q2, q3)
dgeomBCD(x, y, q1, q2, q3)

Arguments

x

value of $X$ that must be non-negative integer

y

value of $Y$ that must be non-negative integer

q1

probability parameter for $X$ , in $(0, 1]$

q2

probability parameter for $Y$ , in $(0, 1]$

q3

dependence parameter, in $(0, 1]$

Details

The joint p.m.f. of the BGCD is:

$P(X = x, Y = y) = K(q_1, q_2, q_3) q_1^x q_2^y q_3^{xy},$

where $K(q_1, q_2, q_3)$ is the normalizing constant computed by the function normalize_constant_BGCD.

Note that:

- $q_3 < 1$ : indicates the negative correlation between $X$ and $Y$

- $q_3 = 1$ : indicates the independence between $X$ and $Y$

Value

The probability $P(X = x, Y = y)$ for each pair of $x$ and $y$ .

References

Ghosh, I., Marques, F., & Chakraborty, S.(2023) A bivariate geometric distribution via conditional specification: properties and applications, Communications in Statistics - Simulation and Computation, 52:12, 5925–5945, doi:10.1080/03610918.2021.2004419

Examples

# Compute P(X = 1, Y = 2) with q1 = 0.5, q2 = 0.6, q3 = 0.8
dgeomBCD(x = 1, y = 2, q1 = 0.5, q2 = 0.6, q3 = 0.8)

# # Compute P(X = 0, Y = 4) with q1 = 0.5, q2 = 0.6, q3 = 0.8
dgeomBCD(x = 0, y = 4, q1 = 0.5, q2 = 0.6, q3 = 0.8)

# Compute P(X = 1, Y = 2) with q1 = 0.5, q2 = 0.6, q3 = 0.8
dgeomBCD(x = 1, y = 2, q1 = 0.5, q2 = 0.6, q3 = 0.8)

# # Compute P(X = 0, Y = 4) with q1 = 0.5, q2 = 0.6, q3 = 0.8
dgeomBCD(x = 0, y = 4, q1 = 0.5, q2 = 0.6, q3 = 0.8)

Joint Probability Mass Function for a Bivariate Poisson Distribution via Conditional Specification

Description

Computes the joint probability mass function (p.m.f.) of a Bivariate Poisson Conditionals distribution (BPCD) based on Ghosh, Marques, and Chakraborty (2021).

Usage

dpoisBCD(x, y, lambda1, lambda2, lambda3)
dpoisBCD(x, y, lambda1, lambda2, lambda3)

Arguments

x

value of $X$ that must be a non-negative integer

y

value of $Y$ that must be a non-negative integer

lambda1

rate parameter for $X$ that must be positive

lambda2

rate parameter for $Y$ that must be positive

lambda3

dependence parameter that must be $(0, 1]$

Details

The joint p.m.f. of the BGCD is

$P(X = x, Y = y) = K(\lambda_1, \lambda_2, \lambda_3) \frac{\lambda_1^x \lambda_2^y \lambda_3^{xy}}{x! y!},$

where $x, y = 0, 1, 2, \ldots$ , and $K(\lambda_1, \lambda_2, \lambda_3)$ is the normalizing constant computed by the function normalize_constant_BPCD.

Key properties of the BPCD include:

- Negative correlation for $\lambda_3 < 1$ ,

- Independence for $\lambda_3 = 1$ .

Value

probability $P(X = x, Y = y)$ for each pair of $x$ and $y$ .

References

Ghosh, I., Marques, F., & Chakraborty, S. (2021). A new bivariate Poisson distribution via conditional specification: properties and applications. Journal of Applied Statistics, 48(16), 3025-3047. doi:10.1080/02664763.2020.1793307

Examples

# Compute P(X = 1, Y = 2) with lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5
dpoisBCD(x = 1, y = 2, lambda1 = 0.5, lambda2 =  0.5, lambda3 =  0.5)

# Compute P(X = 0, Y = 1) with lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5
dpoisBCD(x = 0, y = 1, lambda1 = 0.5, lambda2 =  0.5, lambda3 =  0.5)

# Compute P(X = 1, Y = 2) with lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5
dpoisBCD(x = 1, y = 2, lambda1 = 0.5, lambda2 =  0.5, lambda3 =  0.5)

# Compute P(X = 0, Y = 1) with lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5
dpoisBCD(x = 0, y = 1, lambda1 = 0.5, lambda2 =  0.5, lambda3 =  0.5)

English Premier League Goals (2014–2019)

Description

A list of data frames for five consecutive seasons (2014/15 to 2018/19) from the English Premier League. Each data frame contains the number of full-time home ('X') and away ('Y') goals scored in each match of the season.

Usage

data(eplSeasonGoals)
data(eplSeasonGoals)

Format

A named list of 5 data frames:

1415: 380 rows, variables: X (home goals), Y (away goals)
1516: 380 rows, variables: X (home goals), Y (away goals)
1617: 380 rows, variables: X (home goals), Y (away goals)
1718: 380 rows, variables: X (home goals), Y (away goals)
1819: 380 rows, variables: X (home goals), Y (away goals)
1920: 380 rows, variables: X (home goals), Y (away goals)
2021: 380 rows, variables: X (home goals), Y (away goals)
2122: 380 rows, variables: X (home goals), Y (away goals)
2223: 380 rows, variables: X (home goals), Y (away goals)
2324: 380 rows, variables: X (home goals), Y (away goals)
2525: 380 rows, variables: X (home goals), Y (away goals)

Details

Data source: English Premier League match results from https://football-data.co.uk/ (formerly hosted on datahub.io).

References

Examples

data(eplSeasonGoals)
head(eplSeasonGoals[["1415"]])
head(eplSeasonGoals[["2425"]])
data(eplSeasonGoals)
head(eplSeasonGoals[["1415"]])
head(eplSeasonGoals[["2425"]])

Freeman–Tukey Test for Bivariate Distributions via Conditional Specification

Description

Performs a goodness-of-fit test using the Freeman–Tukey (F–T) statistic for a given dataset and a specified bivariate distribution via Conditional Specification.

Usage

FTtest(data, distribution, params, num_params)
FTtest(data, distribution, params, num_params)

Arguments

data

a dataset or matrix with two columns.

distribution

a string specifying the theoretical distribution ('"BBCD"', '"BBPD"', or '"BBGD"').

params

a named list of parameters required by the specified distribution.

num_params

an integer specifying the number of parameters that were estimated

Details

The Freeman–Tukey (F–T) statistic is used to assess the goodness of fit in contingency tables. It is defined as:

$T^2 = 4 \sum_{i=1}^{r} \sum_{j=1}^{c} \left( \sqrt{O_{ij}} - \sqrt{E_{ij}} \right)^2$

where $O_{ij}$ and $E_{ij}$ are the observed and expected frequencies, respectively.

The statistic $T^2$ asymptotically follows a chi-squared distribution with $(r \cdot c - 1)$ degrees of freedom, where $r$ is the number of rows and $c$ is the number of columns in the contingency table.

Value

A list with components:

observed: Observed frequency table
expected: Expected frequency table under the specified distribution
test: Result of the Freeman–Tukey test, a list with test statistic and p-value

Examples


samples <- rgeomBCD(n = 20, q1 = 0.5, q2 = 0.5, q3 = 0.1, seed = 123)
params <- MLEgeomBCD(samples)
result_bgcd <- FTtest(samples, "BGCD", params, num_params = 3)
result_bgcd

samples <- rpoisBCD(20, lambda1=.5, lambda2=.5, lambda3=.5)
params <- MLEpoisBCD(samples)
result_bpcd <- FTtest(samples, "BPCD", params, num_params = 3)
result_bpcd



samples <- rgeomBCD(n = 20, q1 = 0.5, q2 = 0.5, q3 = 0.1, seed = 123)
params <- MLEgeomBCD(samples)
result_bgcd <- FTtest(samples, "BGCD", params, num_params = 3)
result_bgcd

samples <- rpoisBCD(20, lambda1=.5, lambda2=.5, lambda3=.5)
params <- MLEpoisBCD(samples)
result_bpcd <- FTtest(samples, "BPCD", params, num_params = 3)
result_bpcd

Surface and Interior Faults in 100 Lenses

Description

This dataset records counts of surface faults ( $X$ ) and interior faults ( $Y$ ) observed in 100 optical lenses

Usage

lensfaults
lensfaults

Format

A data frame with 100 rows and 2 variables:

X: Number of surface faults in a lens.
Y: Number of interior faults in the same lens.

References

Aitchison, J., & Ho, C. H. (1989). The multivariate Poisson-log normal distribution. Biometrika, 76(4), 643–653.
Ghosh, I., Marques, F., & Chakraborty, S. (2021). A new bivariate Poisson distribution via conditional specification: properties and applications. Journal of Applied Statistics, 48(16), 3025-3047. doi:10.1080/02664763.2020.1793307

Maximum Likelihood Estimation for a Bivariate Binomial Distribution via Conditional Specification

Description

Estimates the parameters of a Bivariate Binomial Conditionals via Conditional Specification using maximum likelihood.

Usage

MLEbinomBCD(data, fixed_n1 = NULL, fixed_n2 = NULL, verbose = TRUE)
MLEbinomBCD(data, fixed_n1 = NULL, fixed_n2 = NULL, verbose = TRUE)

Arguments

data

A data frame or matrix with columns 'X' and 'Y'

fixed_n1

known value of 'n1' (NULL to estimate)

fixed_n2

known value of 'n2' (NULL to estimate)

verbose

logical; print progress

Value

A list of class "MLEpoisBCD" containing:

n1: estimated n1
n2: estimated n2
p1: estimated p1
p2: estimated p2
lambda: estimated lambda
logLik: Maximum log-likelihood achieved.
AIC: Akaike Information Criterion.
BIC: Bayesian Information Criterion.
convergence: Convergence status from the optimizer (0 means successful).

Examples

data <- rbinomBCD(n = 10,n1 = 5, n2 = 3, p1 = 0.6, p2 = 0.4, lambda = 1.2)
MLEbinomBCD(data)
MLEbinomBCD(data, fixed_n1 = 5, fixed_n2 = 3)

data <- rbinomBCD(n = 10,n1 = 5, n2 = 3, p1 = 0.6, p2 = 0.4, lambda = 1.2)
MLEbinomBCD(data)
MLEbinomBCD(data, fixed_n1 = 5, fixed_n2 = 3)

Maximum Likelihood Estimation for a Bivariate Geometric Distribution via Conditional Specification

Description

Estimates the parameters of a bivariate geometric distribution via Conditional Specification using maximum likelihood.

Usage

MLEgeomBCD(data, initial_values = c(0.5, 0.5, 0.5))
MLEgeomBCD(data, initial_values = c(0.5, 0.5, 0.5))

Arguments

data

data frame or matrix with two columns, representing paired observations of count variables $(X, Y)$ .

initial_values

numeric vector of length 3 with initial values for the parameters q1, q2, and q3. Must be strictly between 0 and 1. Default is c(0.5, 0.5, 0.5).

Details

The model estimates parameters from a joint distribution for $(X, Y)$ with the form:

$P(X = x, Y = y) = K(q_1, q_2, q_3) q_1^x q_2^y q_3^{xy},$

where $K(q_1, q_2, q_3)$ is the normalizing constant.

Value

A list containing:

q1: estimated q1.
q2: estimated q2.
q3: estimated q3.
logLik: Maximum log-likelihood achieved.
AIC: Akaike Information Criterion.
BIC: Bayesian Information Criterion.
convergence: Convergence status from the optimizer (0 means successful).

References

Examples

# Simulate data
samples <- rgeomBCD(n = 50, q1 = 0.2, q2 = 0.2, q3 = 0.5)
result <-MLEgeomBCD(samples)
print(result)
# For better estimation accuracy and stability, consider increasing the sample size (n = 1000)

data(abortflights)
MLEgeomBCD(abortflights)

# Simulate data
samples <- rgeomBCD(n = 50, q1 = 0.2, q2 = 0.2, q3 = 0.5)
result <-MLEgeomBCD(samples)
print(result)
# For better estimation accuracy and stability, consider increasing the sample size (n = 1000)

data(abortflights)
MLEgeomBCD(abortflights)

Maximum Likelihood Estimation for a Bivariate Poisson Distribution via Conditional Specification

Description

Estimates the parameters of a bivariate Poisson distribution via Conditional Specification using maximum likelihood.

Usage

MLEpoisBCD(data, initial_values = NULL)
MLEpoisBCD(data, initial_values = NULL)

Arguments

data

data frame or matrix with two columns, representing paired observations of count variables $(X, Y)$ .

initial_values

optional named list with initial values for the parameters: lambda1, lambda2, and lambda3. If not provided, the function computes heuristic starting values.

Details

The model estimates parameters from a joint distribution for $(X, Y)$ with the form:

$P(X = x, Y = y) = K(\lambda_1, \lambda_2, \lambda_3) \frac{\lambda_1^x \lambda_2^y \lambda_3^{xy}}{x! y!},$

where $x, y = 0, 1, 2, \ldots$ , and $K(\lambda_1, \lambda_2, \lambda_3)$ is the normalizing constant.

Value

A list of class "MLEpoisBCD" containing:

lambda1: estimated lambda1.
lambda2: estimated lambda2.
lambda3: estimated dependence parameter (must be in (0, 1]).
logLik: Maximum log-likelihood achieved.
AIC: Akaike Information Criterion.
BIC: Bayesian Information Criterion.
convergence: Convergence status from the optimizer (0 means successful).

Examples

# Simulate data
data <- rpoisBCD(n = 50, lambda1 = 3, lambda2 = 5, lambda3 = 1)
result <- MLEpoisBCD(data)
print(result)

data(eplSeasonGoals)
MLEpoisBCD(eplSeasonGoals[["1819"]])

data(lensfaults)
MLEpoisBCD(lensfaults)
# Simulate data
data <- rpoisBCD(n = 50, lambda1 = 3, lambda2 = 5, lambda3 = 1)
result <- MLEpoisBCD(data)
print(result)

data(eplSeasonGoals)
MLEpoisBCD(eplSeasonGoals[["1819"]])

data(lensfaults)
MLEpoisBCD(lensfaults)

Cumulative Distribution Function for a Bivariate Binomial Distribution via Conditional Specification

Description

Computes the cumulative distribution function (c.d.f.) of a bivariate binomial conditionals distribution (BBCD) as defined by Ghosh, Marques, and Chakraborty (2025).

Usage

pbinomBCD(x, y, n1, n2, p1, p2, lambda)
pbinomBCD(x, y, n1, n2, p1, p2, lambda)

Arguments

x

value at which the c.d.f. is evaluated

y

value at which the c.d.f. is evaluated

n1

number of trials for $X$ , must be non-negative.

n2

number of trials for $Y$ , must be non-negative.

p1

base success probability for $X$ , in (0, 1).

p2

base success probability for $Y$ , in (0, 1).

lambda

dependence parameter, must be positive.

Value

The probability $P(X \leq x, Y \leq y)$ .

References

Ghosh, I., Marques, F., & Chakraborty, S. (2025). A form of bivariate binomial conditionals distributions. Communications in Statistics - Theory and Methodsm 54(2), 534–553. doi:10.1080/03610926.2024.2315294

Examples

# Compute P(X \le 2, Y \le 1) with n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5
pbinomBCD(x = 2, y = 5, n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5)

# Example with independence (lambda = 1)
pbinomBCD(x = 1, y = 1, n1 = 10, n2 = 10, p1 = 0.3, p2 = 0.6, lambda = 1)

# Compute P(X \le 2, Y \le 1) with n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5
pbinomBCD(x = 2, y = 5, n1 = 5, n2 = 5, p1 = 0.5, p2 = 0.4, lambda = 0.5)

# Example with independence (lambda = 1)
pbinomBCD(x = 1, y = 1, n1 = 10, n2 = 10, p1 = 0.3, p2 = 0.6, lambda = 1)

Cumulative Distribution Function for a Bivariate Geometric Distribution via Conditional Specification

Description

Computes the cumulative distribution function (c.d.f.) of a bivariate geometric conditionals distribution (BGCD) based on Ghosh, Marques, and Chakraborty (2023).

Usage

pgeomBCD(x, y, q1, q2, q3)
pgeomBCD(x, y, q1, q2, q3)

Arguments

x

value at which the c.d.f. is evaluated

y

value at which the c.d.f. is evaluated

q1

probability parameter for $X$ , in (0, 1]

q2

probability parameter for $Y$ , in (0, 1]

q3

dependence parameter, in (0, 1]

Value

The probability $P(X \leq x, Y \leq y)$ .

References

Examples

# Compute P(X \le 1, Y \le 2) with q1 = 0.5, q2 = 0.6, q3 = 0.8
pgeomBCD(x = 1, y = 2, q1 = 0.5, q2 = 0.6, q3 = 0.8)

# Example with small values
pgeomBCD(x = 0, y = 0, q1 = 0.4, q2 = 0.3, q3 = 0.9)

# Compute P(X \le 1, Y \le 2) with q1 = 0.5, q2 = 0.6, q3 = 0.8
pgeomBCD(x = 1, y = 2, q1 = 0.5, q2 = 0.6, q3 = 0.8)

# Example with small values
pgeomBCD(x = 0, y = 0, q1 = 0.4, q2 = 0.3, q3 = 0.9)

Cumulative Distribution Function for a Bivariate Poisson Distribution via Conditional Specification

Description

Computes the cumulative distribution function (c.d.f.) of a bivariate Poisson distribution (BPD) with conditional specification, as described by Ghosh, Marques, and Chakraborty (2021).

Usage

ppoisBCD(x, y, lambda1, lambda2, lambda3)
ppoisBCD(x, y, lambda1, lambda2, lambda3)

Arguments

x

value at which the c.d.f. is evaluated

y

value at which the c.d.f. is evaluated

lambda1

rate parameter for $X$ that must be positive

lambda2

rate parameter for $Y$ that must be positive

lambda3

dependence parameter that must be (0, 1]

Value

The probability $P(X \leq x, Y \leq y)$ .

References

Examples

# Compute P(X \le 1, Y \le 1) with lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5
ppoisBCD(x = 1, y = 1, lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5)

# Example with larger values
ppoisBCD(x = 2, y = 2, lambda1 = 1.0, lambda2 = 1.0, lambda3 = 0.8)

# Compute P(X \le 1, Y \le 1) with lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5
ppoisBCD(x = 1, y = 1, lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5)

# Example with larger values
ppoisBCD(x = 2, y = 2, lambda1 = 1.0, lambda2 = 1.0, lambda3 = 0.8)

Random Sampling from a Bivariate Binomial Distribution via Conditional Specification

Description

Generates random samples from a bivariate binomial conditionals distribution (BBCD).

Usage

rbinomBCD(n, n1, n2, p1, p2, lambda, seed = 123, verbose = TRUE)
rbinomBCD(n, n1, n2, p1, p2, lambda, seed = 123, verbose = TRUE)

Arguments

n

number of samples to generate.

n1

number of trials for $X$ , must be non-negative.

n2

number of trials for $Y$ , must be non-negative.

p1

base success probability for $X$ , in (0, 1).

p2

base success probability for $Y$ , in (0, 1).

lambda

dependence parameter, must be positive.

seed

seed for random number generation (default = 123).

verbose

logical; if TRUE (default), prints progress updates and a summary.

Value

A data frame with columns 'X' and 'Y', containing the sampled values.

Examples

samples <- rbinomBCD(n = 100, n1 = 10, n2 = 10, p1 = 0.5, p2 = 0.4, lambda = 1.2)
head(samples)

samples <- rbinomBCD(n = 100, n1 = 10, n2 = 10, p1 = 0.5, p2 = 0.4, lambda = 1.2)
head(samples)

Random Sampling from a Bivariate Geometric Distribution via Conditional Specification

Description

Generates random samples from a bivariate geometric distribution (BGCD)

Usage

rgeomBCD(n, q1, q2, q3, seed = 123)
rgeomBCD(n, q1, q2, q3, seed = 123)

Arguments

n

number of samples to generate

q1

probability parameter for $X$ , in $(0, 1]$

q2

probability parameter for $Y$ , in $(0, 1]$

q3

dependence parameter, in $(0, 1]$

seed

seed for random number generation (default = 123)

Value

A data frame with two columns: 'X' and 'Y', containing the sampled values.

Examples

# Generate 100 samples
samples <- rgeomBCD(n = 100, q1 = 0.5, q2 = 0.5, q3 = 0.00001)
head(samples)
cor(samples$X, samples$Y)  # Should be negative

# Generate 100 samples
samples <- rgeomBCD(n = 100, q1 = 0.5, q2 = 0.5, q3 = 0.00001)
head(samples)
cor(samples$X, samples$Y)  # Should be negative

Random Sampling from a Bivariate Poisson Distribution via Conditional Specification

Description

Generates random samples from a bivariate Poisson distribution (BPD).

Usage

rpoisBCD(n, lambda1, lambda2, lambda3, seed = 123)
rpoisBCD(n, lambda1, lambda2, lambda3, seed = 123)

Arguments

n

number of samples to generate

lambda1

rate parameter for $X$ that must be positive

lambda2

rate parameter for $Y$ that must be positive

lambda3

dependence parameter that must be (0, 1]

seed

seed for random number generation (default = 123)

Value

A data frame with columns 'X' and 'Y', containing the sampled values.

Examples

samples <- rpoisBCD(n = 100, lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5)
cor(samples$X, samples$Y) # Should be negative

samples <- rpoisBCD(n = 100, lambda1 = 0.5, lambda2 = 0.5, lambda3 = 0.5)
cor(samples$X, samples$Y) # Should be negative

Seed and Plant Count Data

Description

This dataset records the number of seeds sown and the number of resulting plants grown over plots of fixed area (5 square feet).

Usage

seedplant
seedplant

Format

A data frame with n rows and 2 variables:

X: Number of seeds sown.
Y: Number of plants grown.

References

Lakshminarayana, J., S. N. N. Pandit, and K. Srinivasa Rao. 1999. On a bivariate poisson distribution. Communications in Statistics - Theory and Methods, 28 (2), 267–276. doi:10.1080/03610929908832297

Examples

data(seedplant)
head(seedplant)
plot(seedplant$X, seedplant$Y,
     xlab = "Seeds Sown",
     ylab = "Plants Grown",
     main = "Seed vs. Plant Count per Plot")
data(seedplant)
head(seedplant)
plot(seedplant$X, seedplant$Y,
     xlab = "Seeds Sown",
     ylab = "Plants Grown",
     main = "Seed vs. Plant Count per Plot")

Railway Shunter Accident Data (1937–1947)

Description

Accident records for 122 experienced railway shunters across two historical periods.

Usage

shacc
shacc

Format

A data frame with 122 rows and 2 variables:

X: Number of accidents during the 6-year period from 1937 to 1942.
Y: Number of accidents during the 5-year period from 1943 to 1947.

This dataset is useful for analyzing accident rates before and after possible policy or operational changes.

References

Arbous, A. G., & Kerrich, J. E. (1951). Accident statistics and the concept of accident-proneness. Biometrics, 7(4), 340. doi:10.2307/3001656

Examples

data(shacc)
head(shacc)
plot(shacc$X, shacc$Y, xlab = "Accidents 1937–42", ylab = "Accidents 1943–47")
data(shacc)
head(shacc)
plot(shacc$X, shacc$Y, xlab = "Accidents 1937–42", ylab = "Accidents 1943–47")

Package 'BCD'

Help Index

Aborted Flight Counts for 109 Aircrafts

Description

Usage

Format

References

Examples

Joint Probability Mass Function for a Bivariate Binomial Distribution via Conditional Specification

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Joint Probability Mass Function for A Bivariate Geometric Distribution via Conditional Specification

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Joint Probability Mass Function for a Bivariate Poisson Distribution via Conditional Specification

Description

Usage

Arguments

Details

Value

References

See Also

Examples

English Premier League Goals (2014–2019)

Description

Usage

Format

Details

References

Examples

Freeman–Tukey Test for Bivariate Distributions via Conditional Specification

Description

Usage

Arguments

Details

Value

Examples

Surface and Interior Faults in 100 Lenses

Description

Usage

Format

References

Maximum Likelihood Estimation for a Bivariate Binomial Distribution via Conditional Specification

Description

Usage

Arguments

Value

Examples

Maximum Likelihood Estimation for a Bivariate Geometric Distribution via Conditional Specification

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Maximum Likelihood Estimation for a Bivariate Poisson Distribution via Conditional Specification

Description

Usage

Arguments

Details

Value

See Also

Examples

Cumulative Distribution Function for a Bivariate Binomial Distribution via Conditional Specification

Description

Usage