821. general multiple-table data management- Our objective is to develop classes of objects that (1) make handling multiple-table data sets easier and (2) seamlessly integrate with existing R plotting and model-fitting functions; our philosophy is to keep data management and data analysis separate.
822. Testing external pointers in C++- Testing external pointers with finalization in R using C++. Show how to create C++ objects with external pointers from R which are deleted automatically from R. After installing and loading the package in R, type help(testcpp) to get started.
Development Status : 5 - Production/Stable [Filter]
823. Early Warning Signals Toolbox- The Early-Warning-Signals Toolbox provides to the general practitioner available methods for estimating statistical changes in timeseries that can be used for the in-time identification of critical transitions.
824. Models for Data from Unmarked Animals- This is a stub. Development now continues at http://github.com/ianfiske/unmarked.
unmarked analyzes wildlife data arising from many popular wildlife sampling techniques using 2-stage hierarchical models.
827. Vennerable- OBSOLETE.
Development of Vennerable has moved to https://github.com/js229/Vennerable
Vennerable provides Venn diagrams in R. It displays Venn and Euler diagrams for up to 9 different sets and using a variety of geometries
829. Mutoss- Mutoss (multiple hypotheses testing in an open software system) aims at providing a unified, extensible interface covering a large spectrum of multiple hypotheses testing procedures in R. Features a GUI and a simulation tool. Funded by PASCAL2.
830. ORCME- ORCME project develops a package for order restricted clustering of microarray experiments. It reflects on-going research of the bioinformatics group within the Center for Statistics at UHasselt.
837. FactoMineR- FactoMineR performs multivariate EDA. It performs classical methods such as Principal Components Analysis (PCA), Correspondence analysis (CA), Multiple Correspondence Analysis (MCA) and more advanced methods like Multiple Factor Analysis (MFA).
839. Sparse LDA- This package implements elasticnet-like sparseness in linear and mixture discriminant analysis as described in "Sparse Discriminant Analysis" by Line Clemmensen, Trevor Hastie and Bjarne Ersb