A brand new shiny package has entered the world yesterday: shinyalert. It does only one thing, but does it well: show a message to the user in a modal (aka...continue reading.
In my previous post (https://statcompute.wordpress.com/2018/01/28/modeling-lgd-with-proportional-odds-model), I’ve discussed how to use Proportional Odds Models in the LGD model development. In particular, I specifically mentioned that we would estimate a sub-model, which...continue reading.
I’m pleased to announce an update for the sjmisc-package, which was just released on CRAN. Here I want to point out two important changes in the package. New default option...continue reading.
Project Objective Untappd has some usage restrictions for their API, namely not allowing any exploration for analytics or data mining use cases, so I’m going to explore tweets of beer...continue reading.
Similar to COM-Poisson, Double-Poisson, and Generalized Poisson distributions discussed in my previous post (https://statcompute.wordpress.com/2016/11/27/more-about-flexible-frequency-models/), the Hyper-Poisson distribution is another extension of the standard Poisson and is able to accommodate both...continue reading.
The LGD model is an important component in the expected loss calculation. In https://statcompute.wordpress.com/2015/11/01/quasi-binomial-model-in-sas, I discussed how to model LGD with the quasi-binomial regression that is simple and makes no...continue reading.
Exploratory data analysis, data preparation and model performance using ‘funModeling’ R package.continue reading.
Bob Rudis (@hrbrmstr) is a famed expert, author and developer in Data Security and the Chief Security Data Scientist at Rapid7. Bob also creates the most deliciously vivid images of...continue reading.
[crayon-5b7bf0b57c71c617194310-i/] is a useful frequentist approach to hierarchical/multilevel linear regression modelling. For good reason, the model output only includes t-values and doesn’t include p-values (partly due to the difficulty in estimating...continue reading.
A guide through the available drivers and tools when using Amazon Redshift from R and/or RStudio. We examine RPostgreSQL, RPostgres and RJDBC.continue reading.