tag:crantastic.org,2005:/packages/miWQSLatest activity for miWQS2019-12-12T18:22:40Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/960352019-12-12T18:22:40Z2019-12-12T18:22:40ZmiWQS was upgraded to version 0.2.0<a href="/packages/miWQS">miWQS</a> was <span class="action">upgraded</span> to version <a href="/packages/miWQS/versions/91287">0.2.0</a><br /><h3>Package description:</h3><p>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. The `miWQS` package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes. The imputation models are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>) and Bayesian imputation.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/911812019-07-31T05:21:58Z2019-07-31T05:21:58ZmiWQS was upgraded to version 0.1.0<a href="/packages/miWQS">miWQS</a> was <span class="action">upgraded</span> to version <a href="/packages/miWQS/versions/86687">0.1.0</a><br /><h3>Package description:</h3><p>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. The `miWQS` package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes. The two imputation models coded in `miWQS` package are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>) and Bayesian imputation.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/828942018-12-23T16:01:54Z2018-12-23T16:01:54ZmiWQS was released<a href="/packages/miWQS">miWQS</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>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. The `miWQS` package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes. The imputation models are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>) and Bayesian imputation.</p>crantastic.org