Emacs Speaks Statistics (ESS) is an add-on package for GNU Emacs. It is designed to support editing of scripts and interaction with statistical analysis programs: mainly R and SAS. All are welcome to try ESS for free. But advanced users who regularly work with statistical analysis scripts will likely benefit from it the most.

How can I get ESS? Here you can download ESS and related resources. ESS is implemented in the platform-independent programming language Emacs Lisp (eLisp) which is the extension language of Emacs. To run ESS in Emacs you have to first download the compressed eLisp source files and install them according to the following instructions.

Emacs Speaks Statistics (ESS) provides an intelligent, consistent interface between the user and the software. ESS interfaces with R, SAS, S-PLUS, BUGS/JAGS and other statistical analysis packages on GNU/Linux, other Unix-like systems such as macOS, and Microsoft Windows.

Emacs Speaks Statistics (ESS) provides an intelligent, consistent interface between the user and the software. ESS interfaces with SAS, S-PLUS, R, BUGS/JAGS and other statistical analysis packages on Unix, Linux and Microsoft Windows.

This article explains how to start Using R with Emacs and ESS (Emacs Speaks Statistics). The first section of this article provides links to resources on how to install Emacs on various platforms and how to enable it to start writing code in R with the ESS package.

ESS (Emacs Speaks Statistics http://software.biostat.washington.edu/ statsoft/ess/) is an Emacs extension. It is desinged to make S-Plus/R easier to use, especially on Unix platforms. This guide will lead you through some of the most useful ESS features. How to install ESS? ESS is installed under helpdesk.

有个即使bug也不是bug的东西，就是shift+- 这个组合键输出的不是下划线，而是<-，大概是ess或者其他的几个插件的作者特意弄的吧。 http://ishare.iask.sina.com.cn/f/20459778.html 这是我在ubuntu下配置的过程的介绍，做成pdf文档上传到爱问了。

2019年10月1日 · Here is a user-defined R function which computes several variations of rho (ρ ρ) and the ESS formula: M <- length(x) # Re-centre at zero. x <- x - mean(x) # Autocorrelations. rho <- rep(NA, M-2) for(lag in 0:(M-2)){ # Split time-series in two, separated by 'lag' theta.1 <- x[1:(M-lag)] theta.2 <- x[(1+lag):M] # Autocorrelation.

The ESS algorithm is used for model selection in decomposable graphical models. Please use the canonical form https://CRAN.R-project.org/package=ess to link to this page.

Calculates the Effective Sample Size (ESS) for a mixture prior. The ESS indicates how many experimental units the prior is roughly equivalent to.