# Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution

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Another post from R package `misc`

! This time, we’ll see how to fit multiple continuous parametric distributions on a vector of data and simulate best-fitting distribution. Under the hood, `misc::fit_param_dist`

uses a loop of `MASS::fitdistr`

calls and Kullback-Leibler divergence for checking distribution adequacy.

remotes::install_github("thierrymoudiki/misc")

# Example usage 1

set.seed(123) n <- 1000 vector <- rweibull(n, 2, 3) # Replace with your vector start <- proc.time()[3] simulate_function <- misc::fit_param_dist(vector) end <- proc.time()[3] print(paste("Time taken:", end - start)) simulated_data <- simulate_function(n) # Generate 100 samples from the best-fit distribution par(mfrow = c(1, 2)) hist(vector, main = "Original Data", xlab = "Value", ylab = "Frequency") hist(simulated_data, main = "Simulated Data", xlab = "Value", ylab = "Frequency")

# Example usage 2

set.seed(123) n <- 1000 vector <- rnorm(n) # Replace with your vector start <- proc.time()[3] simulate_function <- misc::fit_param_dist(vector) end <- proc.time()[3] print(paste("Time taken:", end - start)) simulated_data <- simulate_function(n) # Generate 1000 samples from the best-fit distribution par(mfrow = c(1, 2)) hist(vector, main = "Original Data", xlab = "Value", ylab = "Frequency") hist(simulated_data, main = "Simulated Data", xlab = "Value", ylab = "Frequency")

# Example usage 3

# Example usage 1 set.seed(123) n <- 1000 vector <- rlnorm(n) # Replace with your vector start <- proc.time()[3] simulate_function <- misc::fit_param_dist(vector) end <- proc.time()[3] print(paste("Time taken:", end - start)) simulated_data <- simulate_function(n) # Generate 1000 samples from the best-fit distribution par(mfrow = c(1, 2)) hist(vector, main = "Original Data", xlab = "Value", ylab = "Frequency") hist(simulated_data, main = "Simulated Data", xlab = "Value", ylab = "Frequency")

# Example usage 4

set.seed(123) n <- 1000 vector <- rbeta(n, 2, 3) # Replace with your vector start <- proc.time()[3] simulate_function <- misc::fit_param_dist(vector, verbose=TRUE) end <- proc.time()[3] print(paste("Time taken:", end - start)) simulated_data <- simulate_function(n) # Generate 1000 samples from the best-fit distribution par(mfrow = c(1, 2)) hist(vector, main = "Original Data", xlab = "Value", ylab = "Frequency") hist(simulated_data, main = "Simulated Data", xlab = "Value", ylab = "Frequency")

**Bonus**: You can develop a package at the command line, by putting this file in the root directory of your package, and typing `make`

or `make help`

at the command line. Here’s the Makefile:

To

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