Package: SensIAT 0.1.1.9000

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_0.1.1.9000.tar.gz
SensIAT_0.1.1.9000.zip(r-4.5)SensIAT_0.1.1.9000.zip(r-4.4)
SensIAT_0.1.1.9000.tgz(r-4.4-x86_64)SensIAT_0.1.1.9000.tgz(r-4.4-arm64)
SensIAT_0.1.1.9000.tar.gz(r-4.5-noble)SensIAT_0.1.1.9000.tar.gz(r-4.4-noble)
SensIAT.pdf |SensIAT.html✨
SensIAT/json (API)
NEWS

# 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

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

SensIAT_example_data - SensIAT Example Data

On CRAN:

4.66 score 1 scripts 117 downloads 4 exports 30 dependencies

Last updated 4 days agofrom:2f26fef129. Checks:OK: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 18 2024
R-4.5-win-x86_64	OK	Nov 18 2024
R-4.5-linux-x86_64	OK	Nov 18 2024
R-4.4-win-x86_64	OK	Nov 18 2024
R-4.4-mac-x86_64	OK	Nov 18 2024
R-4.4-mac-aarch64	OK	Nov 18 2024

Exports:fit_SensIAT_fulldata_model fit_SensIAT_within_group_model SensIAT_jackknife SensIAT_sim_outcome_modeler

Dependencies:assertthat cli cpp11 dplyr fansi generics glue KernSmooth lattice lifecycle magrittr MASS Matrix orthogonalsplinebasis pillar pkgconfig pracma purrr R6 Rcpp rlang stringi stringr survival tibble tidyr tidyselect utf8 vctrs withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Produce fitted model for group (treatment or control)	fit_SensIAT_fulldata_model fit_SensIAT_within_group_model
Compute Conditional Means	pcori_conditional_means
Directly estimate the probability mass function of Y.	pcoriaccel_estimate_pmf
Compiled version of 'evaluate_basis()' function	pcoriaccel_evaluate_basis
Predict mean and variance of the outcome for a 'SensIAT' within-group model	predict.SensIAT_fulldata_model predict.SensIAT_within_group_model
SensIAT Example Data	SensIAT_example_data
Estimate response with jackknife resampling	SensIAT_jackknife
Outcome Modeler for 'SensIAT' Single Index Model.	SensIAT_sim_outcome_modeler