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Factor analysis based on higher order moments
Several factor extraction methods exist based on the covariance matrix. The most well known one being PCA.
Also higher order moments can be used for factor extraction. The most well know approach being ICA.
Several other approaches have been proposed for extracting factors from higher order moments. There is no unified way to do this factor extraction allowing to easily compare results. The goal of this GSOC project is to extend the hofa package and include such a systematic approach.
The hofa package is available at https://github.com/GuanglinHuang/hofa
What other R packages with similar functionality already exist? Why aren't they good enough?
What exactly do you want your student to code in the 3-month deadline? What functions? What do they do? Docs? Tests? Vignettes?
Mentors, please explain how this project will produce a useful package for the R community.
MENTORS: fill in this part. each project needs 2 mentors. One should be an expert R programmer with previous package development experience, and the other can be a domain expert in some other field or application area (optimization, bioinformatics, machine learning, data viz, etc). Ideally one of the two mentors should have previous experience with GSOC (either as a student or mentor). Please provide contact info for each mentor, along with qualifications.
IMPORTANT: you MUST write "EVALUATING" for one mentor, who will be required to do the three evaluations of the student during the summer. In previous years we have had issues with mentors who do not fill in evaluations, and when this happens R project is penalized (money is taken away), although students are not penalized (students are passed by default if no mentor eval is submitted). Therefore one mentor must take responsibility for doing the evaluations, and you must indicate that here, and your student must indicate that as well in the application. If it is not clear which mentor will be the EVALUATING mentor then your project will not be accepted. Example:
Students, please contact mentors below after completing at least one of the tests below.
- EVALUATING MENTOR: Nathan Lassance is an expert in higher order moment modelling.
- Kris Boudt is an expert in higher order moment modelling and coauthors of various R packages. He has extensive experience with mentoring GSOC projects.
Students, please do one or more of the following tests before contacting the mentors above.
MENTORS: write several tests that potential students can do to demonstrate their capabilities for this particular project. Ask some hard questions that will give you insight about how the students write code to solve problems. You'll see that the harder the questions that you ask, the easier it will be for you to choose between the students that apply for your project! Please modify the suggestions below to make them specific for your project.
- Easy: something that any useR should be able to do, e.g. download some existing package listed in the Related Work, and run it on some example data.
- Medium: something a bit more complicated. You can encourage students to write a script or some functions that show their R coding abilities.
- Hard: Can the student write a package with Rd files, tests, and vignettes? If your package interfaces with non-R code, can the student write in that other language?
Students, please post a link to your test results here.
- EXAMPLE STUDENT 1 NAME, LINK TO GITHUB PROFILE, LINK TO TEST RESULTS.