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Visualize 'Confounder' Control in Meta-Analyses
Visualize 'confounder' control in meta-analysis. 'metaconfoundr' is an approach to evaluating bias in studies used in meta-analyses based on the causal inference framework. Study groups create a causal diagram displaying their assumptions about the scientific question. From this, they develop a list of important 'confounders'. Then, they evaluate whether studies controlled for these variables well. 'metaconfoundr' is a toolkit to facilitate this process and visualize the results as heat maps, traffic light plots, and more.
Multivariate Extremes: Bayesian Estimation of the Spectral Measure
Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014)
The Gumbel-Hougaard Copula
Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).
Validation Tools for Artificial Neural Networks
Methods and tools for analysing and validating the outputs and modelled functions of artificial neural networks (ANNs) in terms of predictive, replicative and structural validity. Also provides a method for fitting feed-forward ANNs with a single hidden layer.
Sourcing Archaeological Materials by Chemical Composition
Exploration and analysis of compositional data in the framework of Aitchison (1986, ISBN: 978-94-010-8324-9). This package provides tools for chemical fingerprinting and source tracking of ancient materials.
Wrangle, Analyze, and Visualize Animal Movement Data
Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's 'Motive', the Straw Lab's 'Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli.
Structural Bayesian Information Criterion for Graphical Models
This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review). In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.
R Interface to 'EPP-Lab', a Java Program for Exploratory Projection Pursuit
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package.
Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.)
Small Helpers and Tricks for Epidemics Analysis
A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.