| Name : R-miWQS
| |
| Version : 0.4.4
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.25.2
| Date : 2024-06-14 12:09:02
|
| Group : Development/Libraries/Other
| Source RPM : R-miWQS-0.4.4-lp153.25.2.src.rpm
|
| Size : 0.67 MB
| |
| Packager : (none)
| |
| Summary : Multiple Imputation Using Weighted Quantile Sum Regression
|
Description :
The miWQS package handles the uncertainty due to below the detection
limit in a correlated component mixture problem. Researchers want to
determine if a set/mixture of continuous and correlated
components/chemicals is associated with an outcome and if so, which
components are important in that mixture. These components share a
common outcome but are interval-censored between zero and low
thresholds, or detection limits, that may be different across the
components. This package applies the multiple imputation (MI) procedure
to the weighted quantile sum regression (WQS) methodology for
continuous, binary, or count outcomes (Hargarten & Wheeler (2020)
< doi:10.1016/j.envres.2020.109466>). The imputation models are:
bootstrapping imputation (Lubin et.al (2004) < doi:10.1289/ehp.7199>),
univariate Bayesian imputation (Hargarten & Wheeler (2020)
< doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian
regression imputation.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
