R Packages growth Curve Why R is so popular? There are a lot of reasons, such as: easy to learn and convenient to use, active community, open source, etc. Another important reason is the numerous contributed packages. Up to yesterday, there are 3854 R packages on CRAN. The following figure shows the growth curve of R package:

## Example 9.34: Bland-Altman type plot

The Bland-Altman plot is a visual aid for assessing differences between two ways of measuring something. For example, one might compare two scales this way, or two devices for measuring particulate matter. The plot simply displays the difference between the measures against their average. Rather than a statistical test, it is intended...

## Announcing Revolution R Enterprise 6.0

Revolution Analytics is proud to announce the latest update to our enhanced, production-grade distribution of R, Revolution R Enterprise. This update expands the range of supported computation platforms, adds new Big Data predictive models, and updates to the latest stable release of open source R (2.14.2), which improves performance of the R interpreter by about 30%. This release expands...

## Generate Quasi-Poisson Distribution Variable

Most of regression methods assume that the response variables follow some exponential distribution families, e.g. Guassian, Poisson, Gamma, etc. However, this assumption was frequently violated in real world data by, for example, zero-inflated overdispersion problem. A number of methods were developed to deal with such problem, and among them, Quasi-Poisson and Negative Binomial are the most popular methods perhaps due...

## Fama-MacBeth and Cluster-Robust (by Firm and Time) Standard Errors in R

Ever wondered how to estimate Fama-MacBeth or cluster-robust standard errors in R? It can actually be very easy. First, for some background information read Kevin Goulding’s blog post, Mitchell Petersen’s programming advice, Mahmood Arai’s paper/note and code (there is an earlier version of the code with some more comments in it). For more formal references you may want to

## Example 9.33: Multiple imputation, rounding, and bias

Nick has a paper in the American Statistician warning about bias in multiple imputation arising from rounding data imputed under a normal assumption. One example where you might run afoul of this is if the data are truly dichotomous or count variables, but you model it as normal (either because your software is unable to model dichotomous...

## Optim, you’re doing it wrong?

## Updating to R 2.15, warnings in R and an updated function list for Serious Stats

Whilst writing the book the latest version of R changed several times. Although I started on an earlier version, the bulk of the book was written with 2.11 and it was finished under R 2.12. The final version of the R scripts were therefore run and checked using R 2.12 and, in the main, the most recent

## Temperature reconstruction with useless proxies

In a number of previous posts I considered the temperature proxies that have been used to reconstruct global mean temperatures during the past millenium. In this post I want to show how such a temperature reconstruction would look like if the proxies had no relation at all to the actual temperatures. The motivation is the

## The grade level of Congress speeches, analyzed with R

As widely reported by CNN, the Huffington Post, Talking Points Memo, the sophistication of speeches by US politicians has declined in recent years, dropping from an 11th-grade level in 2005 to a 10th-grade level today. The reports are based on an analysis by the Sunlight Foundation, based on textual analysis of congressional speeches given since 1996 provided by the...