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Thanks for reading. The following table lists recent SAS releases and the versions of R that each supports:. To use R 2.

To use R 3. An error message that you might see because of incompatible versions is "An installed version of R could not be found," although this message can also occur for other reasons. However, after a version of SAS ships, it is impossible to ensure forward compatibility, as evidenced by the R 3. Some software companies distribute their own versions of R, but SAS does not.

## SAS (software)

Consequently, if the interface to R changes, SAS customers need to use a compatible version of R until they can upgrade to a more recent version of SAS. You can call R 3. I'll close with a few comments about bit and bit versions of SAS and R. If you are using SAS software on a bit edition of Windows, you must install the bit edition of R. If you are using SAS software on a bit edition of Windows, you can install either the bit or the bit edition of R. The bit edition of SAS looks first for the bit edition of R and then for the bit edition.

Nominate blog additions by emailing the blog address to webmaster sasCommunity. Enter your email address to subscribe to this blog and receive notifications of new posts by email. Email Address. All entries remain the copyright of the individual contributors. Cellular automata and Conway's Game of Life Conway's Game of Life is a set of simple rules that give rise to beautiful regular and irregular patterns. The following rules were used to evolve the automata: Birth Rule : An organism is born into any empty cell that has exactly three living neighbors.

Survival Rule : An organism with either two or three neighbors survives from one generation to the next.

## WPS Matrix Programming

Death Rule : An organism with four or more neighbors dies from overcrowding. An organism with fewer than two neighbors dies from loneliness.

Two ways to parameterize the lognormal distribution I recently blogged about the relationship between the parameters in the lognormal family and the underlying normal family. Implications for data analysis These pictures and computations tell us a lot about the relationship between the central moments of the normal and the lognormal distribution.

This leads to the following corollaries: Small variations in normal data can lead to big difference in the lognormal data. If you simulate a small sample of data from an N 3, 0. For example, the sample mean might range between 2. However, if you exponentiate the simulated values to form a lognormal distribution, the mean and standard deviation of the lognormal data will vary widely from sample to sample. In short, the mean and standard deviation of lognormal data are very sensitive to variation in the normal samples.

For lognormal data with a large mean, the parameter estimates are not sensitive to variation in the data. Suppose you have a small data set with mean Because of the small sample size, you might be worried that the parameter estimates aren't very good. Variations in the lognormal data are not very important when you estimate the parameters of the underlying normal data. What versions of R are supported by SAS?