Package: SensIAT 0.3.0.9000

Andrew Redd

SensIAT: Sensitivity Analysis for Irregular Assessment Times

Sensitivity analysis for trials with irregular and informative assessment times, based on a new influence function-based, augmented inverse intensity-weighted estimator.

Authors:Andrew Redd [aut, cre], Yujing Gao [aut], Shu Yang [aut], Bonnie Smith [aut], Ravi Varadhan [aut], Agatha Mallett [ctb, ctr], Daniel Scharfstein [pdr, aut], University of Utah [cph]

SensIAT_0.3.0.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SensIAT/json (API)

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

Bug tracker:https://github.com/uofuepibio/sensiat/issues

Pkgdown/docs site:https://uofuepibio.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

6.11 score 2 stars 16 scripts 489 downloads 19 exports 44 dependencies

Last updated from:6fb11997f2. Checks:12 OK, 1 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK460
linux-devel-x86_64OK457
source / vignettesOK296
linux-release-arm64OK462
linux-release-x86_64OK471
macos-release-arm64OK334
macos-release-x86_64OK641
macos-oldrel-arm64OK429
macos-oldrel-x86_64OK815
windows-develERROR480
windows-releaseOK459
windows-oldrelOK445
wasm-releaseOK185

Exports:benchmark_term2_methodscompute_influence_termscompute_SensIAT_expected_valuesextrapolate_from_last_observationfit_marginal_modelfit_SensIAT_fulldata_modelfit_SensIAT_marginal_mean_modelfit_SensIAT_marginal_mean_model_generalizedfit_SensIAT_single_index_fixed_bandwidth_modelfit_SensIAT_single_index_fixed_coef_modelfit_SensIAT_single_index_norm1coef_modelfit_SensIAT_within_group_modeljackknifemake_single_index_simulatormake_term2_integrand_fastparametric_bootstrap_within_groupprepare_SensIAT_datasimulate_SensIAT_datasimulate_SensIAT_two_groups

Dependencies:assertthatBBclassclicpp11dplyrfarvergenericsggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixMAVEmdaorthogonalsplinebasispillarpkgconfigpracmapurrrquadprogR6RColorBrewerRcppRcppArmadillorlangS7scalesstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Fitting with Different Loss and Link Functions
Introduction | Details | Squared Error Loss in the Transformed Space | Identity Link | Log Link | Logit Link | Quasi-likelihood Loss
Rendered fromloss_and_links.Rmdusingknitr::rmarkdown

Last update: 2026-04-15
Started: 2025-10-17

Term2 Integration Methods: Performance Comparison
Overview | Setup: Data and Models | Isolated Term2 Benchmark | Timing Results | Accuracy Results | Visualization | Grid Density Analysis | Recommendations | Use "fast" (default) when: | Use "fixed_grid" when: | Use "seeded_adaptive" when: | Use "original" when: | Session Info
Rendered fromterm2-integration-methods.Rmdusingknitr::rmarkdown

Last update: 2026-04-15
Started: 2026-04-15

Readme and manuals

Help Manual

Help pageTopics
Plot for Estimated Treatment Effect for 'SensIAT_fulldata_jackknife_results' Objectsautoplot.SensIAT_fulldata_jackknife_results
Plot for Estimated Treatment Effect for 'SensIAT_fulldata_model' Objectsautoplot.SensIAT_fulldata_model
Plot a 'SensIAT_within_group_model' Objectautoplot.SensIAT_within_group_model
Plot Bootstrap Marginal Mean Curves with Confidence Bandsautoplot.SensIAT_withingroup_bootstrap_results
Plot Estimates at Given Times for 'SensIAT_withingroup_jackknife_results' Objectsautoplot.SensIAT_withingroup_jackknife_results
Benchmark Term2 Integration Methodsbenchmark_term2_methods
Compute Influence Termscompute_influence_terms compute_influence_terms.default compute_influence_terms.glm compute_influence_terms.SensIAT::Single-index-outcome-model
Compute Conditional Expected Values based on Outcome Modelcompute_SensIAT_expected_values compute_SensIAT_expected_values.glm compute_SensIAT_expected_values.lm compute_SensIAT_expected_values.negbin
Fit SensIAT Marginal Mean Model (Unified)fit_marginal_model
Produce fitted model for group (treatment or control)fit_SensIAT_fulldata_model fit_SensIAT_within_group_model
Fit the Marginal Means Modelfit_SensIAT_marginal_mean_model
Fit the marginal mean model for generalize outcomes.fit_SensIAT_marginal_mean_model_generalized
Outcome Modeler for 'SensIAT' Single Index Model.fit_SensIAT_single_index_fixed_bandwidth_model fit_SensIAT_single_index_fixed_coef_model
Single Index Model using 'MAVE' and Optimizing Bandwidth.fit_SensIAT_single_index_norm1coef_model
Get coefficients for bootstrap simulation.get_bootstrap_coeffs
Perform Jackknife Resampling on an Objectjackknife jackknife.SensIAT_fulldata_model jackknife.SensIAT_within_group_model
Build a parametric intensity simulator using sampled coefficientsmake_parametric_intensity_simulator
Build a parametric outcome simulator from a fitted single-index outcome model using sampled coefficients for the single-index projection.make_parametric_single_index_simulator
Create a simulator function from a fitted Single-index outcome modelmake_single_index_simulator
Parametric bootstrap orchestration *[Experimental]*parametric_bootstrap
Parametric bootstrap for a within-group SensIAT model *[Experimental]*parametric_bootstrap_within_group
Give the Marginal Mean Estimate and its Estimated Asymptotic Variancepredict.SensIAT_fulldata_model predict.SensIAT_within_group_model
Predict Marginal Mean from Bootstrap Coefficient Replicatespredict.SensIAT_withingroup_bootstrap_results
Prepare Data for Sensitivity Analysis with Irregular Assessment Timesprepare_SensIAT_data
Sample parametric coefficients from a fitted model using asymptotic multivariate normal distribution.sample_parametric_coeffs
SensIAT Example DataSensIAT_example_data SensIAT_example_fulldata
Simulate SensIAT Datasimulate_SensIAT_data
Simulate Treatment and Control Groupssimulate_SensIAT_two_groups