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R: A language and environment for statistical computing.
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Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.Abstract:
Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.read more
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mediation: R Package for Causal Mediation Analysis
TL;DR: The mediation package implements a comprehensive suite of statistical tools for conducting causal mediation analysis in applied empirical research and implements a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice.
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Qgraph: Network visualizations of relationships in psychometric data
Sacha Epskamp,Angélique O. J. Cramer,Lourens J. Waldorp,Verena D. Schmittmann,Denny Borsboom +4 more
TL;DR: The qgraph package for R is presented, which provides an interface to visualize data through network modeling techniques, and is introduced by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.
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piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics
TL;DR: In this paper, the authors present an open-source implementation of structural equation models (SEM), a form of path analysis that resolves complex multivariate relationships among a suite of interrelated variables.
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Waste not, want not: why rarefying microbiome data is inadmissible.
Paul J. McMurdie,Susan Holmes +1 more
TL;DR: It is advocated that investigators avoid rarefying altogether and supported statistical theory is provided that simultaneously accounts for library size differences and biological variability using an appropriate mixture model.
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Flexible Imputation of Missing Data
TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.