This week, the post is an interview with Max Marchi. Max is the author, with Jim Albert, of the book "Analyzing baseball data with R". Hi, Max. Welcome back to MilanoR. Last time you wrote for us a series of … Continue reading →

by Joseph Rickert In this roundup of R-related news: Domino enables data science collaboration; Plotly adds an R graphics gallery; Revolution Analytics R user group sponsorship applications are open; and Quandl adds new data sets. San Francisco startup takes on collaborative Data Science Domino, a San Francisco based startup, is inviting users to sign up to beta test its...

Thank you for tuning in! In this post, a continuation of Three Ways to Run Bayesian Models in R, I will: Handwave an explanation of the Laplace Approximation, a fast and (hopefully not too) dirty method to approximate the posterior of a Bayesian model. Show that it is super easy to do Laplace approximation in R, basically four...

Comparing the behavior of the two on the S&P 500. Previously There have been a few posts about Value at Risk (VaR) and Expected Shortfall (ES) including an introduction to Value at Risk and Expected Shortfall. Data and model The underlying data are daily returns for the S&P 500 from 1950 to the present. The VaR and … Continue reading...

american children of the nineties might have had pogs, beanie babies, m.c. hammer, but we lacked a reliable source for state-level survey estimates on health. then in 2003, the maternal and child health bureau of the health services and resources...

Welcome to the first part of my series blog post. In this post, I will discuss about how to implement linear regression step by step in R by understanding the concept of regression. I will try to explain the concept of linear regression in very short manner and try to convert mathematical formulas in to codes(hope you The post Linear...