This is an integrated meta-analysis package for conducting
a research synthesis with correlational data. One of the unique
features of this package is in its integration of user-friendly
functions to complete all statistical steps involved in a
meta-analysis with correlations. It uses recommended procedures
as described in The Handbook of Research Synthesis and
Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

This is an integrated meta-analysis package for conducting
a research synthesis with correlational data. One of the unique
features of this package is in its integration of user-friendly
functions to complete the majority of statistical steps
involved in a meta-analysis with correlations. It uses
recommended procedures as described in The Handbook of Research
Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine,
2009).

This is an integrated meta-analysis package for conducting
a correlational research synthesis. One of the unique features
of this package is in its integration of user-friendly
functions to facilitate statistical analyses at each stage in a
meta-analysis with correlations. It uses recommended procedures
as described in The Handbook of Research Synthesis and
Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

This is an integrated package for conducting all required
steps in a meta-analysis with correlations. It uses recommended
procedures as described in Cooper, Hedges, & Valentine's
Handbook of Research Synthesis and Meta-Analysis (2009).

Class prediction based on microarray data and clinical parameters
R (>= 2.4.1), party, plsgenomics, st, e1071
'Maclinical' implements class prediction using both microarray data and clinical parameters. It addresses the question of the additional predictive value of microarray data.  Class prediction is performed using a two-step method combining (pre-validated) PLS dimension reduction and random forests.

Class prediction based on microarray data and clinical
parameters
'Maclinical' implements class prediction using both
microarray data and clinical parameters. It addresses the
question of the additional predictive value of microarray data.
Class prediction is performed using a two-step method combining
(pre-validated) PLS dimension reduction and random forests.