modelling rare events with small sample size I have a dataset of 600 observations and the target variable is binary(risky/ non-risky). The constraint I have while modelling is that the target variable is very imbalanced .i.e only 12 (only 2% incidence rate) of these observations are risky and others are non risky . I have gone through literature on rare events and have found out that logistic regression for rare events might produce biased estimates if the number of observations with incidence is low (in my case its 12)
To overcome this bias in the estimating process, people started to use penalized regrssion like firth regression (if incidence rate is low but number of observations with incidence is higher, like 2% of 100000 observations is 2000) . And in the case of the sample size  and incidence rate both being low(like 3-6%), literature suggests to use something called as "Exact Logistic Regression" which use MCMC sampling.
Since I do my modelling generally in R, I had found tutorials wherein they had used "elrm" package in R to fit logistic regression. But unfortunately "elrm" package has been removed from the R Cran - Repository. Hence that made me question the credibility of the Exact Logistic Regression for "Small Sample Size Rare  Events(incidence rate around 3-8%)". It would be of great help to me , if you folks could provide me some insights on the working mechanism of Exact Logistic Regression and  whether should I be using regular Logistic Regression (non-penalized estimation) or Exact Logistic Regression (penalized estimation)   for my dataset of 600  observations and 2% incidence rate.
It would also be of great help to me if you could provide me insights on the methods ( apart from firth regression and Exact Logistic Regression)which are generally used to model rare events for small sample size? 
 A: One obvious possibility for dealing with having very little information in the dataset you analyze is to use Bayesian methods, particularly if you have prior information on the question under analysis. In most programming languages there are packages that allow you to do that (e.g. rstan, or perhaps easier rstanarm or brms in R, proc mcmc in SAS). In fact, the Firth penalized likelihood regression is equivalent to Bayesian maximum a-posteriori estimation with Jeffreys prior.
Note that without informative priors you will struggle to do much with very sparse data (such as just 12 cases out of 600), unless you are only investigating a single factor that is associated with a huge effect size.
By the way, exact logistic regression does not normally use MCMC sampling, you simply can approximate it quite well using MCMC sampling. Perhaps the impression that this is the standard approach arises, because there simply is no R package that implements any other approach? In contrast e.g. SAS or StatXact have "real" exact logistic regression.
