14 functions
Monte Carlo simulations and probabilistic modeling.
Functions
Generate a pair of correlated random variables (Stochastic Information Packets) for Monte Carlo simulations.
Fit a flexible Metalog distribution to empirical data for Monte Carlo simulation.
Generate a flexible Metalog distribution from specified percentiles for stochastic simulation.
Generate a long-format DataFrame of stochastic trials (SIPs) for a list of items.
Generate a SLURP distribution for future forecasts using a fitted Exponential Smoothing model.
Generate a SLURP distribution for future outcomes using a fitted Linear Regression model.
Generate a SLURP distribution for predicted probabilities using a fitted Logistic Regression model.
Simulate the number of successes in a series of independent Bernoulli trials (Binomial Distribution).
Simulate discrete event counts over a fixed interval (Poisson Distribution).
Simulate the number of independent trials required to achieve the first success.
Run Monte Carlo simulations for normally distributed outcomes.
Simulate continuous variables following a Student's T distribution.
Simulate the duration of time between consecutive, independent events (Exponential Distribution).
Simulate the total time required for a specific number of events to occur (Gamma Distribution).
Quantify uncertainty with Monte Carlo simulations. Fit distributions to expert judgment, model correlated variables, and generate thousands of scenarios from a few inputs.