University of Minnesota,
Twin Cities
School of Statistics
Charlie Geyer's Home Page

Department of Ecology, Evolution, and Behavior
Ruth Shaw's Home Page

Chicago Botanic Garden
Institute for Plant Conservation Biology
Stuart Wagenius's Home Page

Echinacea Project Page
Aster Project Page

Two University of Minnesota (Twin Cities) faculty members are looking for undergraduates interested in research on aster models. They are Ruth Shaw in the Department of Ecology, Evolution, and Behavior and Charles Geyer in the School of Statistics.

The home page for the aster project is here.

One project, suitable for biology students with an interest in evolutionary genetics or ecology, is aster analysis of life history data. These would be like the aster analyses in the two published papers linked on the home page but some details would be different.

Several data sets are available and waiting for an analysis.

The very short introduction of aster is that it is a generalization of linear regression, binomial and Poisson regression, and survival analysis that allows life history data to be used for estimation of population growth, for estimation of fitness landscapes, and for many other purposes.

For a somewhat longer introduction, read the introduction and discussion
sections of the new paper to appear in *American
Naturalist*.

Some knowledge of statistics is necessary.

One project, suitable for computer science students or other skilled
programmers is work on the R package `aster`

.

R (`www.r-project.org`

)
is to statistics as C is to general programming.
Both were invented at Bell Labs. Both have the
unix nature.
It is the
language
of choice for research statistics.

Unlike C, the R language is interpreted, garbage collected, and has dynamic typing (like Perl, Python, or Scheme). It is a Turing complete programming language with some functional and object-oriented features.

R is free software distributed from the CRAN web site (similar to CTAN for TeX and CPAN for Perl). It supports extensions and hundreds of R contributed packages that deal with specific statistical problems are available and trivial for users to load into R and use.

R
contributed package `aster`

has existed for about three years
and has sufficed to do the analyses in the two existing papers.

However the first paper described a more general class of models than the package actually implements and it is unclear how to refactor the existing code base to implement this more general class of models, so some serious programming is needed.

R is implemented in C. Extension packages often use C called from R.
The current version of the `aster`

package has about 1300 lines
of R and about 2700 lines of C. Since C is a lot harder to learn than R,
some knowledge of C is necessary. R is simple enough to learn on the project.