boostmath (version 1.4.0)

negative_binomial_distribution: Negative Binomial Distribution Functions

Description

Functions to compute the probability density function, cumulative distribution function, and quantile function for the Negative Binomial distribution.

Usage

negative_binomial_distribution(successes, success_fraction)
negative_binomial_pdf(x, successes, success_fraction)
negative_binomial_lpdf(x, successes, success_fraction)
negative_binomial_cdf(x, successes, success_fraction)
negative_binomial_lcdf(x, successes, success_fraction)
negative_binomial_quantile(p, successes, success_fraction)
negative_binomial_find_lower_bound_on_p(trials, successes, alpha)
negative_binomial_find_upper_bound_on_p(trials, successes, alpha)
negative_binomial_find_minimum_number_of_trials(
  failures,
  success_fraction,
  alpha
)
negative_binomial_find_maximum_number_of_trials(
  failures,
  success_fraction,
  alpha
)

Value

A single numeric value with the computed probability density, log-probability density, cumulative distribution, log-cumulative distribution, or quantile depending on the function called.

Arguments

successes

number of successes (successes >= 0)

success_fraction

probability of success on each trial (0 <= success_fraction <= 1)

x

quantile

p

probability (0 <= p <= 1)

trials

number of trials

alpha

significance level (0 < alpha < 1)

failures

number of failures (failures >= 0)

See Also

Boost Documentation for more details on the mathematical background.

Examples

# Negative Binomial distribution with successes = 5, success_fraction = 0.5
dist <- negative_binomial_distribution(5, 0.5)
# Apply generic functions
cdf(dist, 0.5)
logcdf(dist, 0.5)
pdf(dist, 0.5)
logpdf(dist, 0.5)
hazard(dist, 0.5)
chf(dist, 0.5)
mean(dist)
median(dist)
mode(dist)
range(dist)
quantile(dist, 0.2)
standard_deviation(dist)
support(dist)
variance(dist)
skewness(dist)
kurtosis(dist)
kurtosis_excess(dist)

# Convenience functions
negative_binomial_pdf(3, 5, 0.5)
negative_binomial_lpdf(3, 5, 0.5)
negative_binomial_cdf(3, 5, 0.5)
negative_binomial_lcdf(3, 5, 0.5)
negative_binomial_quantile(0.5, 5, 0.5)

if (FALSE) {
# Find lower bound on p given 10 trials and 5 successes with 95% confidence
negative_binomial_find_lower_bound_on_p(10, 5, 0.05)
# Find upper bound on p given 10 trials and 5 successes with 95% confidence
negative_binomial_find_upper_bound_on_p(10, 5, 0.05)
# Find minimum number of trials to observe 3 failures with success fraction 0.5 at 95% confidence
negative_binomial_find_minimum_number_of_trials(3, 0.5, 0.05)
# Find maximum number of trials to observe 3 failures with success fraction 0.5 at 95% confidence
negative_binomial_find_maximum_number_of_trials(3, 0.5, 0.05)
}