| Name : R-selectMeta
| |
| Version : 1.0.8
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.4.7
| Date : 2024-06-14 11:23:07
|
| Group : Development/Libraries/Other
| Source RPM : R-selectMeta-1.0.8-lp153.4.7.src.rpm
|
| Size : 0.09 MB
| |
| Packager : (none)
| |
| Summary : Estimation of Weight Functions in Meta Analysis
|
Description :
Publication bias, the fact that studies identified for inclusion in a
meta analysis do not represent all studies on the topic of interest, is
commonly recognized as a threat to the validity of the results of a
meta analysis. One way to explicitly model publication bias is via
selection models or weighted probability distributions. In this package
we provide implementations of several parametric and nonparametric
weight functions. The novelty in Rufibach (2011) is the proposal of a
non-increasing variant of the nonparametric weight function of Dear &
Begg (1992). The new approach potentially offers more insight in the
selection process than other methods, but is more flexible than
parametric approaches. To maximize the log-likelihood function proposed
by Dear & Begg (1992) under a monotonicity constraint we use a
differential evolution algorithm proposed by Ardia et al (2010a, b) and
implemented in Mullen et al (2009). In addition, we offer a method to
compute a confidence interval for the overall effect size theta,
adjusted for selection bias as well as a function that computes the
simulation-based p-value to assess the null hypothesis of no selection
as described in Rufibach (2011, Section 6).
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
