mixR: An R package for Finite Mixture Modeling for Both Raw and Binned Data
نویسندگان
چکیده
منابع مشابه
mixtools: An R Package for Analyzing Finite Mixture Models
The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture models. In the latter category, mixtools provides algorithms for estimating parameters in a wide rang...
متن کاملBayesMix: An R package for Bayesian Mixture Modeling
The R package BayesMix provides the functionality for estimating univariate Gaussian mixture models with MCMC methods. Within a given model class users can modify the prior specifications and the initial values for developing a suitable model for their data. Furthermore, tools for analyzing the output of the MCMC simulations as, e.g., diagnostic plots, are available. The package is intended to ...
متن کاملAn R package for analyzing and modeling ranking data
BACKGROUND In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statisti...
متن کاملrebmix: An R Package for Continuous and Discrete Finite Mixture Models
The rebmix package for R provides functions for random univariate and multivariate finite mixture generation, number of components, component weights and component parameters estimation, bootstrapping and plotting of the finite mixtures. It relies on the REBMIX algorithm that requires preprocessing, information criterion and conditionally independent normal, lognormal, Weibull, gamma, binomial,...
متن کاملRebayes: an R Package for Empirical Bayes Mixture Methods
Models of unobserved heterogeneity, or frailty as it is commonly known in survival analysis, can often be formulated as semiparametric mixture models and estimated by maximum likelihood as proposed by Robbins (1950) and elaborated by Kiefer and Wolfowitz (1956). Recent developments in convex optimization, as noted by Koenker and Mizera (2014b), have led to dramatic improvements in computational...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of open source software
سال: 2022
ISSN: ['2475-9066']
DOI: https://doi.org/10.21105/joss.04031