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NBLDA
View on CRAN: Click
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Download and install NBLDA package within the R console
Install from CRAN:
install.packages("NBLDA")
Install from Github:
library("remotes")
install_github("cran/NBLDA") Install by package version:
library("remotes")
install_version("NBLDA", "1.0.1") Attach the package and use:
library("NBLDA")
Maintained by
Dincer Goksuluk
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First Published: 2018-07-01
Latest Update: 2022-02-21
Description:
We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version.
How to cite:
Dincer Goksuluk (2018). NBLDA: Negative Binomial Linear Discriminant Analysis. R package version 1.0.1, https://cran.r-project.org/web/packages/NBLDA. Accessed 05 Aug. 2026.
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Complete documentation for NBLDA
Functions, R codes and Examples using
the NBLDA R package
Some associated functions: FindBestTransform . NBLDA-package . NullModel . cervical . control . generateCountData . getShrinkedDispersions . inputs . nblda-class . nbldaControl . nbldaTrained . nblda_input-class . nblda_trained-class . normalization . plot . predict . selectedFeatures . show . trainNBLDA .
Some associated R codes: FindBestTransform.R . all_classes.R . all_generics.R . control.R . copiedFromOtherPackages.R . generateCountData.R . getShrinkedDispersions.R . helper_functions.R . normalize_counts.R . package_and_supplementary.R . plot.nblda.R . predict.nblda.R . trainNBLDA.R . zzz_methods.R . Full NBLDA package functions and examples
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