In UBC-MDS/BlackBoxR:

Introduction

The Bayesian vs Frequentist approach is more of a philosophical debate which this package will not delve into. This package attempts at breaking down the understanding and the underlying assumptions of the 2 approaches and how they compare. The package will run a significance analysis using both approaches based on data provided by the user, compare credible and confidence intervals and finally debunks the understanding of MAP and MLE for parameter estimation.

This package is aimed at users who are attempting to familiarize themselves with the Bayesian/Frequentist approach(although I'm guessing it will be more Bayesian). This package can elucidate the difference in approaches and will attempt to help the user get a basic high-level understanding of both approaches and how they should proceed to carry out further analysis.

Functions Included

getCredibleInterval() : Perform Monte-Carlo estimation to obtain credible intervals

getConfidenceInterval() : Obtain confidence interval for the result

performABTest() : Run A\B test using the Frequentist approach

getMLE(): Get maximum likelihood value of the parameter for a given distribution.

Work in Progress

performABtest_Bayesian() : Run A\B test using the Bayesian approach

getMAP(): Get Maximum a Priori estimate for the parameters for a given distribution.

Example Usage

getMLE(distribution,column)

*Purpose*: compute the log likelihood of data given the distribution

*Args*:

distribution: type of distribution of the data. for example (bernoulli, poisson). Support for 2 as of now.

column: the column is a vector of numeric data over which we perform the maximum likelihood

*Return*:

the log likelihood of the data.For example, mean for poisson, probability for bernoulli

performABTest(data,alpha = 0.05)

*Purpose*: Performs A/B testing based on the data. Uses Fisher's exact test or Chi-square based on the size of the data

*Args*:

data - input dataframe consisting of A and B events and occurance of events

alpha - false positive rate

*Returns*

p-value indicating significance
Graph displaying the change in p-values over trials
Method used to compute p-value

Also a quote using >:

"He who gives up [code] safety for [code] speed deserves neither." (via)

UBC-MDS/BlackBoxR documentation built on May 25, 2019, 1:35 p.m.