my_e2trunct

Compute second moment of the truncated t. Uses results from O'Hagan, Biometrika, 1973

The R package 'ashr' implements an Empirical Bayes
approach for large-scale hypothesis testing and false discovery
rate (FDR) estimation based on the methods proposed in
M. Stephens, 2016, "False discovery rates: a new deal",
<DOI:10.1093/biostatistics/kxw041>. These methods can be applied
whenever two sets of summary statistics---estimated effects and
standard errors---are available, just as 'qvalue' can be applied
to previously computed p-values. Two main interfaces are
provided: ash(), which is more user-friendly; and ash.workhorse(),
which has more options and is geared toward advanced users. The
ash() and ash.workhorse() also provides a flexible modeling
interface that can accommodate a variety of likelihoods (e.g.,
normal, Poisson) and mixture priors (e.g., uniform, normal).

Peter Carbonetto

ashr

Methods for Adaptive Shrinkage, using Empirical Bayes

Matthew Stephens

Chaoxing Dai

David Gerard

Mengyin Lu

Lei Sun

Jason Willwerscheid

Nan Xiao

Mazon Zeng

my_e2trunct function

<dl><dt>a</dt>
<dd>left limit of distribution</dd>
<dt>b</dt>
<dd>right limit of distribution</dd>
<dt>df</dt>
<dd>degree of freedom of error distribution</dd></dl>

Arguments

my_e2trunct — my_e2trunct

<dl>

<dt>a</dt>
<dd>left limit of distribution</dd>


<dt>b</dt>
<dd>right limit of distribution</dd>


<dt>df</dt>
<dd>degree of freedom of error distribution</dd>

</dl>

my_e2trunct: my_e2trunct

Description

Usage

Arguments