Package: CopSens 0.1.0

Jiajing Zheng

CopSens: Copula-Based Sensitivity Analysis for Observational Causal Inference

Implements the copula-based sensitivity analysis method, as discussed in Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding <arxiv:2102.09412>, with Gaussian copula adopted in particular.

Authors:Jiajing Zheng [aut, cre], Alexander Franks [aut], Alex D'Amour [ctb]

CopSens_0.1.0.tar.gz
CopSens_0.1.0.zip(r-4.5)CopSens_0.1.0.zip(r-4.4)CopSens_0.1.0.zip(r-4.3)
CopSens_0.1.0.tgz(r-4.4-any)CopSens_0.1.0.tgz(r-4.3-any)
CopSens_0.1.0.tar.gz(r-4.5-noble)CopSens_0.1.0.tar.gz(r-4.4-noble)
CopSens_0.1.0.tgz(r-4.4-emscripten)CopSens_0.1.0.tgz(r-4.3-emscripten)
CopSens.pdf |CopSens.html✨
CopSens/json (API)

# Install 'CopSens' in R:

install.packages('CopSens', repos = c('https://jiajingz.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jiajingz/copsens/issues

Datasets:

GaussianT_BinaryY - Dataset with Gaussian Treatments and Binary Outcomes
GaussianT_GaussianY - Dataset with Gaussian Treatments and Outcomes
mice_est_nulltr - Estimates of genes' effects on mice body weight using null treatments approach from Miao et al.
micedata - Body weight and gene expressions of 287 mice

On CRAN:

3.18 score 3 stars 7 scripts 183 downloads 4 exports 117 dependencies

Last updated 2 years agofrom:0f6e60c06b. Checks:OK: 5 NOTE: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 17 2024
R-4.5-win	NOTE	Nov 17 2024
R-4.5-linux	NOTE	Nov 17 2024
R-4.4-win	OK	Nov 17 2024
R-4.4-mac	OK	Nov 17 2024
R-4.3-win	OK	Nov 17 2024
R-4.3-mac	OK	Nov 17 2024

Exports:bcalibrate cal_rv gcalibrate plot_estimates

Dependencies:askpass backports base64enc Biobase BiocGenerics bit bit64 blob broom bslib cachem callr cellranger cli clipr colorspace conflicted cpp11 crayon curl CVXR data.table DBI dbplyr digest dplyr dtplyr ECOSolveR evaluate fansi farver fastmap fontawesome forcats fs gargle generics ggplot2 glue gmp googledrive googlesheets4 gtable haven highr hms htmltools httr ids isoband jquerylib jsonlite knitr labeling lattice lifecycle lubridate magrittr MASS Matrix memoise mgcv mime modelr munsell nlme openssl osqp pcaMethods pillar pkgconfig prettyunits processx progress ps purrr R6 ragg rappdirs RColorBrewer Rcpp RcppEigen readr readxl rematch rematch2 reprex rlang rmarkdown Rmpfr rstudioapi rvest sass scales scs selectr stringi stringr sys systemfonts textshaping tibble tidyr tidyselect tidyverse timechange tinytex tzdb utf8 uuid vctrs viridisLite vroom withr xfun xml2 yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calibration for Binary Outcomes	bcalibrate
Calculate Robustness Value When Executing Worstcase Calibration	cal_rv
Calibrate Estimate of Intervention Mean for Binary Outcome	cali_mean_ybinary_algm
Dataset with Gaussian Treatments and Binary Outcomes	GaussianT_BinaryY
Dataset with Gaussian Treatments and Outcomes	GaussianT_GaussianY
Calibration for Gaussian Outcomes	gcalibrate
Obtain Optimized Sensitivity Parameters Using Multivariate Calibration Criterion	get_opt_gamma
Estimates of genes' effects on mice body weight using null treatments approach from Miao et al. (2020)	mice_est_nulltr
Body weight and gene expressions of 287 mice	micedata
Visualize Estimates of Treatment Effects	plot_estimates