Q-value estimation for false discovery rate control
Alan Dabney <adabney@u.washington.edu> and
John D. Storey <jstorey@u.washington.edu>, with assistance from
Gregory R. Warnes <gregory_r_warnes@groton.pfizer.com>
This package takes a list of p-values resulting from the
simultaneous testing of many hypotheses and estimates their
q-values. The q-value of a test measures the proportion of
false positives incurred (called the false discovery rate)
when that particular test is called significant. Various plots
are automatically generated, allowing one to make sensible
significance cut-offs. Several mathematical results have recently
been shown on the conservative accuracy of the estimated
q-values from this software. The software can be applied
to problems in genomics, brain imaging, astrophysics, and
data mining.

Alan Dabney <adabney@u.washington.edu> and John D. Storey
<jstorey@u.washington.edu>, with assistance from Gregory R.
Warnes <gregory_r_warnes@groton.pfizer.com>
This package takes a list of p-values resulting from the
simultaneous testing of many hypotheses and estimates their
q-values. The q-value of a test measures the proportion of
false positives incurred (called the false discovery rate) when
that particular test is called significant. Various plots are
automatically generated, allowing one to make sensible
significance cut-offs. Several mathematical results have
recently been shown on the conservative accuracy of the
estimated q-values from this software. The software can be
applied to problems in genomics, brain imaging, astrophysics,
and data mining.

Quasi-variances for factor effects in statistical models
Functions to compute quasi-variances and associated measures of approximation error

Quasi variances for factor effects in statistical models
Functions to compute quasi variances and associated measures of approximation error

Functions to compute quasi variances and associated
measures of approximation error

Quasi Variances for Factor Effects in Statistical Models