This deliverable describes several statistical approaches that provide insights into real-life mixtures. In addition, it provides statistical methods that can be used to gain insight into the determinants of mixture profiles in HBM data. The deliverable provides practical instructions on how to apply the statistical methods using the R language. At each step we provide a demonstration application using a dataset that was simulated to represent a real-life example of HBM data. A brief description of the potential inferences that can be made when applying the methods on real-life data is also provided
Humans are exposed to large number of chemicals from various sources in their environment. Current r...
The aim of this work was to exemplify the inclusion of human biomonitoring (HBM) data in risk assess...
R code and output from the analysis of two data examples using the two statistical approaches descri...
This deliverable focuses on preparatory ground work associated with the work on the identification o...
This report describes the results obtained using network analysis in combination with human biomoni...
R code and output for the analysis of two publicly available datasets obtained from binary mixture e...
In order to evaluate a proof-of-concept for the identification of mixture health effects, a selected...
Human health risk assessment of chemical mixtures is complex due to the almost infinite number of po...
This report describes the purpose of mixture risk assessment case studies conducted in the context o...
The mixtools package for R provides a set of functions for analyzing a variety of finite mixture mod...
Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure...
The aim of this work was to demonstrate how human biomonitoring (HBM) data can be included in differ...
Co-authors: Henriqueta Louro, Bruno Costa Gomes, Maria João Silva, Ricardo Assunção, Carla Martins,...
Mixture models have been around for over 150 years, and they are found in many branches of statistic...
depmixS4 implements a general framework for defining and estimating dependent mixture models in the ...
Humans are exposed to large number of chemicals from various sources in their environment. Current r...
The aim of this work was to exemplify the inclusion of human biomonitoring (HBM) data in risk assess...
R code and output from the analysis of two data examples using the two statistical approaches descri...
This deliverable focuses on preparatory ground work associated with the work on the identification o...
This report describes the results obtained using network analysis in combination with human biomoni...
R code and output for the analysis of two publicly available datasets obtained from binary mixture e...
In order to evaluate a proof-of-concept for the identification of mixture health effects, a selected...
Human health risk assessment of chemical mixtures is complex due to the almost infinite number of po...
This report describes the purpose of mixture risk assessment case studies conducted in the context o...
The mixtools package for R provides a set of functions for analyzing a variety of finite mixture mod...
Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure...
The aim of this work was to demonstrate how human biomonitoring (HBM) data can be included in differ...
Co-authors: Henriqueta Louro, Bruno Costa Gomes, Maria João Silva, Ricardo Assunção, Carla Martins,...
Mixture models have been around for over 150 years, and they are found in many branches of statistic...
depmixS4 implements a general framework for defining and estimating dependent mixture models in the ...
Humans are exposed to large number of chemicals from various sources in their environment. Current r...
The aim of this work was to exemplify the inclusion of human biomonitoring (HBM) data in risk assess...
R code and output from the analysis of two data examples using the two statistical approaches descri...