Package: CGNM 0.9.0

Yasunori Aoki

CGNM: Cluster Gauss-Newton Method

Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gauss–Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67.

Authors:Yasunori Aoki

CGNM_0.9.0.tar.gz
CGNM_0.9.0.tar.gz(r-4.5-noble)CGNM_0.9.0.tar.gz(r-4.4-noble)
CGNM_0.9.0.tgz(r-4.4-emscripten)CGNM_0.9.0.tgz(r-4.3-emscripten)
CGNM.pdf |CGNM.html
CGNM/json (API)

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

Peer review:

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

2.48 score 6 scripts 656 downloads 25 exports 51 dependencies

Last updated 5 months agofrom:a56f335014. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-linuxOKNov 10 2024

Exports:acceptedApproximateMinimizersacceptedIndicesacceptedIndices_binaryacceptedMaxSSRbestApproximateMinimizersCluster_Gauss_Newton_Bootstrap_methodCluster_Gauss_Newton_methodcol_quantileplot_2DprofileLikelihoodplot_goodnessOfFitplot_paraDistribution_byHistogramplot_paraDistribution_byViolinPlotsplot_parameterValue_scatterPlotsplot_profileLikelihoodplot_Rank_SSRplot_simulationMatrixWithCIplot_simulationWithCIplot_SSR_parameterValueplot_SSRsurfaceshinyCGNMsuggestInitialLowerRangesuggestInitialUpperRangetable_parameterSummarytable_profileLikelihoodConfidenceIntervaltopIndices

Dependencies:base64encbslibcachemclicolorspacecommonmarkcrayondigestfansifarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigpromisesR6rappdirsRColorBrewerRcpprlangsassscalesshinysourcetoolstibbleutf8vctrsviridisLitewithrxtable

CGNM: Cluster Gauss-Newton Method

Rendered fromCGNM-vignette.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2024-06-13
Started: 2022-03-14

Readme and manuals

Help Manual

Help pageTopics
acceptedApproximateMinimizersacceptedApproximateMinimizers
acceptedIndicesacceptedIndices
acceptedIndices_binaryacceptedIndices_binary
acceptedMaxSSRacceptedMaxSSR
bestApproximateMinimizersbestApproximateMinimizers
Cluster_Gauss_Newton_Bootstrap_methodCluster_Gauss_Newton_Bootstrap_method
Cluster_Gauss_Newton_methodCluster_Gauss_Newton_method
col_quantilecol_quantile
plot_2DprofileLikelihoodplot_2DprofileLikelihood
plot_goodnessOfFitplot_goodnessOfFit
plot_paraDistribution_byHistogramplot_paraDistribution_byHistogram
plot_paraDistribution_byViolinPlotsplot_paraDistribution_byViolinPlots
plot_parameterValue_scatterPlotsplot_parameterValue_scatterPlots
plot_profileLikelihoodplot_profileLikelihood
plot_Rank_SSRplot_Rank_SSR
plot_simulationMatrixWithCIplot_simulationMatrixWithCI
plot_simulationWithCIplot_simulationWithCI
plot_SSR_parameterValueplot_SSR_parameterValue
plot_SSRsurfaceplot_SSRsurface
shinyCGNMshinyCGNM
suggestInitialLowerRangesuggestInitialLowerRange
suggestInitialUpperRangesuggestInitialUpperRange
table_parameterSummarytable_parameterSummary
table_profileLikelihoodConfidenceIntervaltable_profileLikelihoodConfidenceInterval
topIndicestopIndices