Package: tehtuner 0.3.2

tehtuner: Fit and Tune Models to Detect Treatment Effect Heterogeneity

Implements methods to fit Virtual Twins models (Foster et al. (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.

Authors:Jack Wolf [aut, cre]

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tehtuner.pdf |tehtuner.html
tehtuner/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/jackmwolf/tehtuner/issues

Datasets:

On CRAN:

clinical-trialsheterogeneity-of-treatment-effectsubgroup-identification

3.30 score 4 stars 6 scripts 141 downloads 1 exports 106 dependencies

Last updated 1 years agofrom:bbacf0f019. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:tunevt

Dependencies:base64encbitbit64bitopsbslibcachemcaToolsclicliprcodetoolscoincolorspacecpp11crayoncvAUCdata.tabledata.treeDiagrammeRdigestdplyrearthevaluatefansifarverfastmapfontawesomeforeachFormulafsgamgenericsglmnetgluegplotsgtoolshighrhmshtmltoolshtmlwidgetsigraphiteratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelibcoinlifecyclemagrittrMASSMatrixmatrixStatsmemoisemimemodeltoolsmultcompmunsellmvtnormnnlspartypillarpkgconfigplotmoplotrixprettyunitsprogresspurrrR6randomForestSRCrappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreadrrlangrmarkdownROCRrpartrstudioapisandwichsassscalesshapestringistringrstrucchangeSuperLearnersurvivalTH.datatibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevisNetworkvroomwithrxfunyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Get the MNPP for the Step 2 modelget_mnpp
Get the MNPP for a Classification Treeget_mnpp.classtree
Get the MNPP for a Conditional Inference Treeget_mnpp.ctree
Get the MNPP for a Model fit via Lassoget_mnpp.lasso
Get the MNPP for a Regression Treeget_mnpp.rtree
Permute a dataset under the null hypothesis and get the MNPPget_theta_null
Get the appropriate Step 1 estimation function associated with a methodget_vt1
Get the appropriate Step 2 estimation function associated with a methodget_vt2
Generate a dataset with permuted treatment indicatorspermute
Print an object of class tunevtprint.tunevt
Simulated example datatehtuner_example
Test if a Value Gives a Null Conditional Inference Treetest_null_theta_ctree
Estimate the penalty parameter for Step 2 of Virtual Twinstune_theta
Fit a tuned Virtual Twins modeltunevt
Check if alpha0 is a valid input to tunevtvalidate_alpha0
Check if p_reps is a valid input to tunevtvalidate_p_reps
Check if Trt is a valid input to tunevtvalidate_Trt
Check if Y is a valid input to tunevtvalidate_Y
Estimate the CATE Using the Lasso for Step 1 of Virtual Twinsvt1_lasso
Estimate the CATE Using MARS for Step 1 of Virtual Twinsvt1_mars
Estimate the CATE Using a Random Forest for Step 1 of Virtual Twinsvt1_rf
Estimate the CATE Using Super Learner for Step 1 of Virtual Twinsvt1_super
Estimate the CATE using a classification tree for Step 2vt2_classtree
Estimate the CATE using a conditional inference tree for Step 2vt2_ctree
Estimate the CATE using the Lasso for Step 2vt2_lasso
Estimate the CATE using a regression tree for Step 2vt2_rtree