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gmmsslm
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Download and install gmmsslm package within the R console
Install from CRAN:
install.packages("gmmsslm")
Install from Github:
library("remotes")
install_github("cran/gmmsslm")
Install by package version:
library("remotes")
install_version("gmmsslm", "1.1.5")
Attach the package and use:
library("gmmsslm")
Maintained by
Ziyang Lyu
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-02-16
Latest Update: 2023-10-16
Description:
The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.
How to cite:
Ziyang Lyu (2023). gmmsslm: Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism. R package version 1.1.5, https://cran.r-project.org/web/packages/gmmsslm. Accessed 21 Nov. 2024.
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Complete documentation for gmmsslm
Functions, R codes and Examples using
the gmmsslm R package
Some associated functions: bayesclassifier . bootstrap_gmmsslm . cov2vec . discriminant_beta . erate . errorrate . gastro_data . get_clusterprobs . get_entropy . gmmsslm . gmmsslmFit-class . initialvalue . list2par . loglk_full . loglk_ig . loglk_miss . logsumexp . makelabelmatrix . neg_objective_function . normalise_logprob . par2list . paraextract . plot_missingness . predict . pro2vec . rlabel . rmix . summary . vec2cov . vec2pro .
Some associated R codes: bayesclassifier.R . bootstrap_gmmsslm.R . cov2vec.R . discriminant_beta.R . erate.R . errorrate.R . gastro_data.R . get_clusterprobs.R . get_entropy.R . gmmsslm.R . initialvalue.R . list2par.R . loglk_full.R . loglk_ig.R . loglk_miss.R . logsumexp.R . makelabelmaatrix.R . neg_objective_function.R . normalise_logprob.R . par2list.R . plot_missingness.R . pro2vec.R . rlabel.R . rmix.R . vec2cov.R . vec2pro.R . Full gmmsslm package functions and examples
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