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Bayesian Inference of TKTD Models
Advanced methods for a valuable quantitative environmental risk
assessment using Bayesian inference of survival Data with toxicokinetics
toxicodynamics (TKTD) models. Among others, it facilitates Bayesian inference of
the general unified threshold model of survival (GUTS). See models description
in Jager et al. (2011)
Elevation and GPS Data Visualisation
Simpler processing of digital elevation model and GPS trace data for use with the 'rayshader' package.
Univariate and Multivariate Model-Based Clustering in Group-Specific Functional Subspaces
The funHDDC algorithm allows to cluster functional univariate (Bouveyron and Jacques, 2011,
Extended Structural Equation Modelling
Create structural equation models that can be manipulated programmatically.
Models may be specified with matrices or paths (LISREL or RAM)
Example models include confirmatory factor, multiple group, mixture
distribution, categorical threshold, modern test theory, differential
Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares.
equations, state space, and many others.
Support and advanced package binaries available at < https://openmx.ssri.psu.edu>.
The software is described in Neale, Hunter, Pritikin, Zahery, Brick,
Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016)
Tools for Trade Practitioners
A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001)
Estimating Abundance of Clones from DNA Fragmentation Data
Estimate the abundance of cell clones from the distribution of lengths of DNA fragments (as created by sonication, whence `sonicLength'). The algorithm in "Estimating abundances of retroviral insertion sites from DNA fragment length data" by Berry CC, Gillet NA, Melamed A, Gormley N, Bangham CR, Bushman FD. Bioinformatics; 2012 Mar 15;28(6):755-62 is implemented. The experimental setting and estimation details are described in detail there. Briefly, integration of new DNA in a host genome (due to retroviral infection or gene therapy) can be tracked using DNA sequencing, potentially allowing characterization of the abundance of individual cell clones bearing distinct integration sites. The locations of integration sites can be determined by fragmenting the host DNA (via sonication or fragmentase), breaking the newly integrated DNA at a known sequence, amplifying the fragments containing both host and integrated DNA, sequencing those amplicons, then mapping the host sequences to positions on the reference genome. The relative number of fragments containing a given position in the host genome estimates the relative abundance of cells hosting the corresponding integration site, but that number is not available and the count of amplicons per fragment varies widely. However, the expected number of distinct fragment lengths is a function of the abundance of cells hosting an integration site at a given position and a certain nuisance parameter. The algorithm implicitly estimates that function to estimate the relative abundance.
Modelling Reproduction and Survival Data in Ecotoxicology
Advanced methods for a valuable quantitative environmental risk
assessment using Bayesian inference of survival and reproduction Data. Among
others, it facilitates Bayesian inference of the general unified
threshold model of survival (GUTS). See our companion paper
Baudrot and Charles (2021)
Estimation of Multinormal Mixture Distribution
Fit multivariate mixture of normal distribution using covariance structure.
'VigiBase' Pharmacovigilance Database Toolbox
Perform the analysis of the World Health Organization
(WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version),
< https://who-umc.org/>
e.g., load data, perform data management,
disproportionality analysis, and descriptive statistics. Intended for
pharmacovigilance routine use or studies.
This package is NOT supported nor reflect the opinion of the WHO, or the
Uppsala Monitoring Centre.
Disproportionality methods are described by Norén et
al (2013)
Framework for Specifying and Simulating Individual Based Models
A framework which provides users a set of useful primitive elements for specifying individual based simulation models, with special attention models for infectious disease epidemiology. Users build models by specifying variables for each characteristic of individuals in the simulated population by using data structures exposed by the package. The package provides efficient methods for finding subsets of individuals based on these variables, or cohorts. Cohorts can then be targeted for variable updates or scheduled for events. Variable updates queued during a time step are executed at the end of a discrete time step, and the code places no restrictions on how individuals are allowed to interact. These data structures are designed to provide an intuitive way for users to turn their conceptual model of a system into executable code, which is fast and memory efficient.