---
title: "The Bivariate Geometric Conditionals Distribution (BGCD)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{The Bivariate Geometric Conditionals Distribution (BGCD)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(BCD)
```

# Introduction

This vignette introduces the Bivariate Geometric Conditionals Distribution (BGCD), defined via conditional specifications, as proposed by Ghosh, Marques, and Chakraborty (2023). The `BCD` package provides functions to evaluate the joint and cumulative distributions, perform random sampling, and estimate parameters via maximum likelihood.

# Joint Probability: `dgeomBCD()`

The joint probability mass function (p.m.f.) of the BBCD is given by:

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

where \( K \) is a normalizing constant ensuring the probabilities sum to 1 and 
\eqn{ x, y = 0, 1, 2, \ldots }.

Note that: $q_3 < 1$indicates the negative correlation between $X$ and $Y$, while
$q_3 = 1$ indicates the independence between $X$ and $Y$.

## Example

```{r}
dgeomBCD(x = 1, y = 2, q1 = 0.5, q2 = 0.6, q3 = 0.8)
dgeomBCD(x = 0, y = 4, q1 = 0.5, q2 = 0.6, q3 = 0.8)
```

# Cumulative Distribution: `pgeomBCD()`

The function `pgeomBCD()` computes the cumulative distribution:

\[
P(X \leq x, Y \leq y)
\]

## Example

```{r}
pgeomBCD(x = 1, y = 2, q1 = 0.5, q2 = 0.6, q3 = 0.8)
pgeomBCD(x = 0, y = 0, q1 = 0.4, q2 = 0.3, q3 = 0.9)
```

# Random Sampling: `rpoisBCD()`

Generate samples from the BPCD using:

```r
rgeomBCD(n, q1, q2, q3)
```

## Example

```{r}
set.seed(123)
samples <- rgeomBCD(n = 100, q1 = 0.5, q2 = 0.5, q3 = 0.1)
head(samples)
cor(samples$X, samples$Y)  # Should be negative
```

# Maximum Likelihood Estimation: `MLEgeomBCD()`

Estimate the parameters of the distribution from data.

## Example

```{r}
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)

```{r}
samples <- rgeomBCD(n = 1000, q1 = 0.2, q2 = 0.2, q3 = 0.5)
result <-MLEgeomBCD(samples)
print(result)
```

# Real Data Example 
The dataset `abortflights` 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.

```{r}
data(abortflights)
head(abortflights)
table(abortflights$X, abortflights$Y)
```

```{r}
fit <- MLEgeomBCD(abortflights)
FTtest(abortflights, "BGCD", params = fit, num_params = 3)
```


**Reference:**
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.
