This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Title: Gaussian Parsimonious Clustering Models with Covariates and aĭescription: Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020). Package MoEClust updated to version 1.4.0 with previous version 1.3.3 dated ĭiff between SAMTx versions 0.1.0 dated and 0.2.0 dated (2020) A flexible sensitivity analysis approach for unmeasured confounding with a multiple treatments and a binary outcome. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference. Bayesian Additive Regression Model (BART) is used for outcome modeling. Nested multiple imputation is embedded within the Bayesian framework to integrate uncertainty about the sensitivity parameters and sampling variability. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Title: Sensitivity Assessment to Unmeasured Confounding with Multipleĭescription: A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. Package SAMTx updated to version 0.2.0 with previous version 0.1.0 dated