README.md

In gastonstat/binomial: Functions for Binomial Distribution

Overview

"binomial" is a minimal R package that provides functions for calculating probabilities of a Binomial random variable, and related calculations such as the probability distribution, the expected value, variance, mode, etc.

Donation

As a Data Science and Statistics educator, I love to share the work I do. Each month I spend dozens of hours curating learning materials and computational tools like this R package. If you find any value and usefulness in binomial, please consider making a one-time donation---via paypal---in any amount (e.g. the amount you would spend inviting me a coffee or any other drink). Your support really matters.

Motivation

The creation of this package was used as a project assignment in one of the editions of Stat 133 Concepts in Computing with Data. The main purpose of this assignment was to illustrate some of the concepts behind the creation of an R package.

Installation

Install the development version from GitHub via the package "devtools":

# development version from GitHub:
install.packages("devtools") 

# install "binomial"
devtools::install_github("gastonstat/binomial")

Usage

library(binomial)

# probability of getting 2 successes in 5 trials
# (assuming prob of success = 0.5)
bin_probability(success = 2, trials = 5, prob = 0.5)
#> [1] 0.3125

# probabilities of getting 2 or less successes in 5 trials
# (assuming prob of success = 0.5)
bin_probability(success = 0:2, trials = 5, prob = 0.5)
#> [1] 0.03125 0.15625 0.31250

# 55 heads in 100 tosses of a loaded coin with 45% chance of heads
bin_probability(success = 55, trials = 100, prob = 0.45)
#> [1] 0.01075277

 # binomial probability distribution
bin_distribution(trials = 5, prob = 0.5)
#>   success probability
#> 1       0     0.03125
#> 2       1     0.15625
#> 3       2     0.31250
#> 4       3     0.31250
#> 5       4     0.15625
#> 6       5     0.03125

# plotting binomial probability distribution
dis1 <- bin_distribution(trials = 5, prob = 0.5)
plot(dis1)

 # binomial cumulative distribution
bin_cumulative(trials = 5, prob = 0.5)
#>   success probability cumulative
#> 1       0     0.03125    0.03125
#> 2       1     0.15625    0.18750
#> 3       2     0.31250    0.50000
#> 4       3     0.31250    0.81250
#> 5       4     0.15625    0.96875
#> 6       5     0.03125    1.00000

Binomial Random Variable

The package "binomial" also has the function bin_variable() that lets you create a binomial random variable. To be more precise, this function returns an object of class "binvar"

# binomial variable
bin1 <- bin_variable(trials = 10, p = 0.3)
bin1
#> "Binomial variable"
#> 
#> Paramaters 
#> - number of trials: 10 
#> - prob of success : 0.3

# summary method
binsum1 <- summary(bin1)
binsum1
#> "Summary Binomial"
#> 
#> Paramaters 
#> - number of trials: 10 
#> - prob of success : 0.3 
#> 
#> Measures 
#> - mean    : 3 
#> - variance: 2.1 
#> - mode    : 3 
#> - skewness: 0.2760262 
#> - kurtosis: -0.1238095

Summary Meausres

Finally, "binomial" has a couple of additional functions to compute summary measures such as the mean, variance, kurtosis

# mean
bin_mean(10, 0.3)
#> [1] 3

# variance
bin_variance(10, 0.3)
#> [1] 2.1

# mode
bin_mode(10, 0.3)
#> [1] 3

# skewness
bin_skewness(10, 0.3)
#> [1] 0.2760262

# kurtosis
bin_kurtosis(10, 0.3)
#> [1] -0.1238095

gastonstat/binomial documentation built on April 5, 2022, 6:37 a.m.