Package: ihclust 0.1.0

ihclust: Iterative Hierarchical Clustering (IHC)

Provides a set of tools to i) identify geographic areas with significant change over time in drug utilization, and ii) characterize common change over time patterns among the time series for multiple geographic areas. For reference, see below: 1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) <doi:10.1504/IJCBDD.2018.10011910> 2. Wu, S., Wu, H. (2013) <doi:10.1186/1471-2105-14-6> 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) <doi:10.1016/j.idm.2016.07.001>.

Authors:Elin Cho [aut, cre], Yuting Xu [aut], Jaejoon Song [aut]

ihclust_0.1.0.tar.gz
ihclust_0.1.0.zip(r-4.5)ihclust_0.1.0.zip(r-4.4)ihclust_0.1.0.zip(r-4.3)
ihclust_0.1.0.tgz(r-4.4-any)ihclust_0.1.0.tgz(r-4.3-any)
ihclust_0.1.0.tar.gz(r-4.5-noble)ihclust_0.1.0.tar.gz(r-4.4-noble)
ihclust_0.1.0.tgz(r-4.4-emscripten)ihclust_0.1.0.tgz(r-4.3-emscripten)
ihclust.pdf |ihclust.html
ihclust/json (API)

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

Peer review:

Bug tracker:https://github.com/elincho/ihclust/issues

Datasets:

On CRAN:

3 exports 0.63 score 114 dependencies 1 scripts 214 downloads

Last updated 2 years agofrom:5e88f0c15d. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 12 2024
R-4.5-winWARNINGSep 12 2024
R-4.5-linuxWARNINGSep 12 2024
R-4.4-winWARNINGSep 12 2024
R-4.4-macWARNINGSep 12 2024
R-4.3-winWARNINGSep 12 2024
R-4.3-macWARNINGSep 12 2024

Exports:ihclustsimcurvetestchange

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacliclustercodetoolscolorspacecorrplotcowplotcpp11crosstalkdendextendDerivdigestdoBydoParalleldplyrDTellipseemmeansestimabilityevaluatefactoextraFactoMineRfansifarverfastmapflashClustfontawesomeforeachfsgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehighrhtmltoolshtmlwidgetshttpuvisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrpolynompromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenreshape2rlangrmarkdownrstatixsassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisviridisLitewithrxfunyaml