Package: tehtuner 0.3.3

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]

tehtuner_0.3.3.tar.gz
tehtuner_0.3.3.zip(r-4.7)tehtuner_0.3.3.zip(r-4.6)tehtuner_0.3.3.zip(r-4.5)
tehtuner_0.3.3.tgz(r-4.6-any)tehtuner_0.3.3.tgz(r-4.5-any)
tehtuner_0.3.3.tar.gz(r-4.7-any)tehtuner_0.3.3.tar.gz(r-4.6-any)
tehtuner_0.3.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
tehtuner/json (API)
NEWS

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

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

Datasets:

On CRAN:

Conda:

clinical-trialsheterogeneity-of-treatment-effectsubgroup-identification

3.48 score 6 stars 6 scripts 181 downloads 1 exports 103 dependencies

Last updated from:3824aedbea. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK192
source / vignettesOK206
linux-release-x86_64OK170
macos-release-arm64OK134
macos-oldrel-arm64OK196
windows-develOK106
windows-releaseOK173
windows-oldrelOK125
wasm-releaseOK138

Exports:tunevt

Dependencies:base64encbitbit64bitopsbslibcachemcaToolsclicliprcodetoolscoincpp11crayoncvAUCdata.tabledata.treeDiagrammeRdigestdplyrearthevaluatefarverfastmapfontawesomeforeachFormulafsgamgenericsglmnetgluegplotsgtoolshighrhmshtmltoolshtmlwidgetsigraphiteratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelibcoinlifecyclemagrittrMASSMatrixmatrixStatsmemoisemimemodeltoolsmultcompmvtnormnnlspartypillarpkgconfigplotmoplotrixprettyunitsprogresspurrrR6randomForestSRCrappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreadrrlangrmarkdownROCRrpartrstudioapisandwichsassscalesshapestringistringrstrucchangeSuperLearnersurvivalTH.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