Package: bayespm 0.2.0

bayespm: Bayesian Statistical Process Monitoring

The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" <doi:10.1080/00224065.2021.1916413>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" <doi:10.1080/00224065.2022.2161434>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" <doi:10.1080/00224065.2022.2161435>.

Authors:Dimitrios Kiagias [aut, cre, cph], Konstantinos Bourazas [aut, cph], Panagiotis Tsiamyrtzis [aut, cph]

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bayespm.pdf |bayespm.html
bayespm/json (API)

# Install 'bayespm' in R:
install.packages('bayespm', repos = c('https://kiagiasdim.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • ECE - ECE dataset for the PCC process for Poisson with rate parameter unknown
  • aPTT - Dataset for PCC process for Normal with both parameters unknown

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

26 exports 0.09 score 33 dependencies 222 downloads

Last updated 1 years agofrom:c021e6605f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 05 2024
R-4.5-winOKSep 05 2024
R-4.5-linuxOKSep 05 2024
R-4.4-winOKSep 05 2024
R-4.4-macOKSep 05 2024
R-4.3-winOKSep 05 2024
R-4.3-macOKSep 05 2024

Exports:betabinom_HMbetanbinom_HMbinom_PCCbinom_PRCbinom_PRC_hcompgamma_HDgamma_PCCgb2_HDinvgamma_PCClnorm_HDlnorm1_PCClnorm2_PCClnorm3_PCClt_HDnbinom_HMnbinom_PCCnorm_HDnorm_mean2_PRCnorm_mean2_PRC_hnorm1_PCCnorm2_PCCnorm3_PCCpois_PCCpois_PRCpois_PRC_ht_HD

Dependencies:clicolorspaceextraDistrfansifarverggplot2gluegridExtragtableinvgammaisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcpprlangrmutilscalestibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Dataset for PCC process for Normal with both parameters unknownaPTT
The Highest Mass (HM) interval of Beta-Binomial distribution.betabinom_HM
The Highest Mass (HM) interval of Beta-Negative Binomial distribution.betanbinom_HM
PCC for Binomial data with probability parameter unknownbinom_PCC
PRC for Binomial data with probability parameter unknownbinom_PRC
Derivation of the decision limit for the PRC for Binomial data with probability parameter unknownbinom_PRC_h
The Highest Density (HD) interval of Compound Gamma distribution.compgamma_HD
ECE dataset for the PCC process for Poisson with rate parameter unknownECE
PCC for Gamma data with rate parameter unknowngamma_PCC
The Highest Density (HD) interval of Generalized Beta of the second kind distribution.gb2_HD
PCC for Inverse-Gamma data with scale parameter unknowninvgamma_PCC
The Highest Density (HD) interval of Lognormal distribution.lnorm_HD
PCC for LogNormal data with scale parameter unknownlnorm1_PCC
PCC for LogNormal data with shape parameter unknownlnorm2_PCC
PCC for LogNormal data with both parameters unknownlnorm3_PCC
The Highest Density (HD) interval of Logt distribution.lt_HD
The Highest Mass (HM) interval of Beta-Negative Binomial distribution.nbinom_HM
PCC for Negative Binomial data with probability parameter unknownnbinom_PCC
The Highest Density (HD) interval of Normal distribution.norm_HD
PRC for Normal data with unknown parameters (mean)norm_mean2_PRC
Derivation of the decision limit for the PRC for Normal data with unknown parameters (mean)norm_mean2_PRC_h
PCC for Normal data with mean unknownnorm1_PCC
PCC for Normal data with variance unknownnorm2_PCC
PCC for Normal data with both parameters unknownnorm3_PCC
PCC for Poisson data with rate parameter unknownpois_PCC
PRC for Poisson data with rate parameter unknownpois_PRC
Derivation of the decision limit for the PRC for Poisson data with probability parameter unknownpois_PRC_h
The Highest Density (HD) interval of Student's t distribution.t_HD