Package: jmBIG 0.1.2
jmBIG: Joint Longitudinal and Survival Model for Big Data
Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.
Authors:
jmBIG_0.1.2.tar.gz
jmBIG_0.1.2.zip(r-4.5)jmBIG_0.1.2.zip(r-4.4)jmBIG_0.1.2.zip(r-4.3)
jmBIG_0.1.2.tgz(r-4.4-any)jmBIG_0.1.2.tgz(r-4.3-any)
jmBIG_0.1.2.tar.gz(r-4.5-noble)jmBIG_0.1.2.tar.gz(r-4.4-noble)
jmBIG_0.1.2.tgz(r-4.4-emscripten)jmBIG_0.1.2.tgz(r-4.3-emscripten)
jmBIG.pdf |jmBIG.html✨
jmBIG/json (API)
# Install 'jmBIG' in R: |
install.packages('jmBIG', repos = c('https://atanubhattacharjee.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 months agofrom:8a9a8fefc4. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 16 2024 |
R-4.5-win | OK | Nov 16 2024 |
R-4.5-linux | OK | Nov 16 2024 |
R-4.4-win | OK | Nov 16 2024 |
R-4.4-mac | OK | Nov 16 2024 |
R-4.3-win | OK | Nov 16 2024 |
R-4.3-mac | OK | Nov 16 2024 |
Exports:cisurvfitJMCSjmbayesBigjmcsBigjmstanBigjoinRMLBigplot_cisurvfitJMCSpostSurvfitpostTrajpredJMbayespredJRMLprintsurvfitJMCS
Dependencies:abindbackportsbase64encbayesplotBHbootbslibcachemcallrcaretcheckmateclasscliclockclustercmprskcobscodacodetoolscolorspacecolourpickercommonmarkcpp11crayoncrosstalkdata.tabledescdiagramdigestdistributionaldoParalleldplyrDTdygraphse1071evaluatefansifarverFastJMfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2ggridgesGLMMadaptiveglobalsgluegowergridExtragtablegtoolshardhathighrHmischtmlTablehtmltoolshtmlwidgetshttpuvigraphinlineipredisobanditeratorsJMbayes2joineRMLjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelistenvlme4loolubridatemagrittrmarkdownMASSMatrixMatrixModelsmatrixStatsmemoisemetsmgcvmimeminiUIminqaModelMetricsmultcompmunsellmvtnormnlmenloptrnnetnumDerivparallellypecpillarpkgbuildpkgconfigplotrixplyrpolsplineposteriorpROCprocessxprodlimprogressrpromisesproxypsPublishpurrrquantregQuickJSRR6randtoolboxrangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrecipesreshape2riskRegressionrlangrmarkdownrmsrngWELLrpartrstanrstanarmrstantoolsrstudioapisandwichsassscalesshapeshinyshinyjsshinystanshinythemessourcetoolsSparseMSQUAREMStanHeadersstatmodstringistringrsurvivaltensorATH.datathreejstibbletidyrtidyselecttimechangetimeDatetimeregtimeROCtinytextzdbutf8vctrsviridisviridisLitewithrxfunxtablextsyamlzoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Bootstrapped CI using 'FastJM' | cisurvfitJMCS |
Joint model for BIG data using JMbayes2 | jmbayesBig |
Joint model for BIG data using FastJM | jmcsBig |
Joint model for BIG data using rstanarm | jmstanBig |
Joint model for BIG data using joineRML | joinRMLBig |
longitudinal data | long2 |
longitudinal- survival dataset | longsurv |
Plot for 'cisurvfitJMCS' object | plot_cisurvfitJMCS |
Prediction using 'rstanarm' | postSurvfit |
Prediction using 'rstanarm' | postTraj |
Prediction using 'JMbayes2' | predJMbayes |
Prediction using 'joineRML' | predJRML |
print.jmbayesBig | print.jmbayesBig |
print.jmcsBig | print.jmcsBig |
print.jmstanBig | print.jmstanBig |
print.joinRMLBig | print.joinRMLBig |
survival data | surv2 |
Prediction using 'FastJM' | survfitJMCS |