CRAN Package Check Results for Package DeclareDesign

Last updated on 2024-04-20 08:50:49 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.8 8.91 243.09 252.00 ERROR
r-devel-linux-x86_64-debian-gcc 1.0.8 5.67 174.71 180.38 ERROR
r-devel-linux-x86_64-fedora-clang 1.0.8 297.29 ERROR
r-devel-linux-x86_64-fedora-gcc 1.0.8 307.83 ERROR
r-prerel-macos-arm64 1.0.8 88.00 OK
r-prerel-macos-x86_64 1.0.8 270.00 OK
r-prerel-windows-x86_64 1.0.8 8.00 193.00 201.00 OK
r-patched-linux-x86_64 1.0.8 9.71 231.18 240.89 ERROR
r-release-linux-x86_64 1.0.8 5.98 232.58 238.56 ERROR
r-release-macos-arm64 1.0.8 90.00 OK
r-release-macos-x86_64 1.0.8 225.00 OK
r-release-windows-x86_64 1.0.8 13.00 264.00 277.00 OK
r-oldrel-macos-arm64 1.0.8 90.00 OK
r-oldrel-windows-x86_64 1.0.8 14.00 257.00 271.00 OK

Additional issues

noSuggests

Check Details

Version: 1.0.8
Check: package dependencies
Result: NOTE Package suggested but not available for checking: ‘margins’ Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.0.8
Check: examples
Result: ERROR Running examples in ‘DeclareDesign-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: declare_estimator > ### Title: Declare estimator > ### Aliases: declare_estimator declare_estimators label_estimator > ### method_handler > > ### ** Examples > > > # Setup for examples > design <- + declare_model( + N = 500, + gender = rbinom(N, 1, 0.5), + U = rnorm(N, sd = 0.25), + potential_outcomes(Y ~ rbinom( + N, 1, prob = pnorm(0.2 * Z + 0.2 * gender + 0.1 * Z * gender + U) + )) + ) + + declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) + + declare_sampling(S = complete_rs(N = N, n = 200)) + + declare_assignment(Z = complete_ra(N = N, m = 100)) + + declare_measurement(Y = reveal_outcomes(Y ~ Z)) > > run_design(design) inquiry estimand 1 ATE 0.138 > > # default estimator is lm_robust with tidy summary > design_0 <- + design + + declare_estimator(Y ~ Z, inquiry = "ATE") > > run_design(design_0) inquiry estimand estimator term estimate std.error statistic p.value 1 ATE 0.082 estimator Z 0.01 0.07025926 0.14233 0.8869641 conf.low conf.high df outcome 1 -0.1285525 0.1485525 198 Y > > # Linear regression using lm_robust and tidy summary > design_1 <- + design + + declare_estimator( + formula = Y ~ Z, + .method = lm_robust, + .summary = tidy, + term = "Z", + inquiry = "ATE", + label = "lm_no_controls" + ) > > run_design(design_1) inquiry estimand term estimator estimate std.error statistic p.value 1 ATE 0.106 Z lm_no_controls 0.01 0.06842381 0.146148 0.8839533 conf.low conf.high df outcome 1 -0.124933 0.144933 198 Y > > # Use glance summary function to view model fit statistics > design_2 <- + design + + declare_estimator(.method = lm_robust, + formula = Y ~ Z, + .summary = glance) > > run_design(design_2) inquiry estimand estimator r.squared adj.r.squared statistic p.value 1 ATE 0.062 estimator 0.00490049 -0.0001252651 0.9750754 0.3246228 df.residual nobs se_type 1 198 200 HC2 > > # Use declare_estimator to implement custom answer strategies > my_estimator <- function(data) { + data.frame(estimate = mean(data$Y)) + } > > design_3 <- + design + + declare_inquiry(Y_bar = mean(Y)) + + declare_estimator(handler = label_estimator(my_estimator), + label = "mean", + inquiry = "Y_bar") > > run_design(design_3) inquiry estimand estimator estimate 1 Y_bar 0.570 mean 0.57 2 ATE 0.136 <NA> NA > > # Use `term` to select particular coefficients > design_4 <- + design + + declare_inquiry(difference_in_cates = mean(Y_Z_1[gender == 1] - Y_Z_0[gender == 1]) - + mean(Y_Z_1[gender == 0] - Y_Z_0[gender == 0])) + + declare_estimator(Y ~ Z * gender, + term = "Z:gender", + inquiry = "difference_in_cates", + .method = lm_robust) > > run_design(design_4) inquiry estimand term estimator estimate std.error 1 difference_in_cates 0.1443609 Z:gender estimator 0.1966622 0.1381796 2 ATE 0.1180000 <NA> <NA> NA NA statistic p.value conf.low conf.high df outcome 1 1.423236 0.1562576 -0.07584754 0.469172 196 Y 2 NA NA NA NA NA <NA> > > # Use glm from base R > design_5 <- + design + + declare_estimator(Y ~ Z + gender, + family = "gaussian", + inquiry = "ATE", + .method = glm) > > run_design(design_5) inquiry estimand estimator term estimate std.error statistic p.value 1 ATE 0.074 estimator Z 0.07 0.07 1 0.3185372 conf.low conf.high 1 -0.06719748 0.2071975 > > # If we use logit, we'll need to estimate the average marginal effect with > # margins::margins. We wrap this up in function we'll pass to model_summary > > library(margins) # for margins Error in library(margins) : there is no package called ‘margins’ Execution halted Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.0.8
Check: examples
Result: ERROR Running examples in ‘DeclareDesign-Ex.R’ failed The error most likely occurred in: > ### Name: declare_estimator > ### Title: Declare estimator > ### Aliases: declare_estimator declare_estimators label_estimator > ### method_handler > > ### ** Examples > > > # Setup for examples > design <- + declare_model( + N = 500, + gender = rbinom(N, 1, 0.5), + U = rnorm(N, sd = 0.25), + potential_outcomes(Y ~ rbinom( + N, 1, prob = pnorm(0.2 * Z + 0.2 * gender + 0.1 * Z * gender + U) + )) + ) + + declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) + + declare_sampling(S = complete_rs(N = N, n = 200)) + + declare_assignment(Z = complete_ra(N = N, m = 100)) + + declare_measurement(Y = reveal_outcomes(Y ~ Z)) > > run_design(design) inquiry estimand 1 ATE 0.138 > > # default estimator is lm_robust with tidy summary > design_0 <- + design + + declare_estimator(Y ~ Z, inquiry = "ATE") > > run_design(design_0) inquiry estimand estimator term estimate std.error statistic p.value 1 ATE 0.082 estimator Z 0.01 0.07025926 0.14233 0.8869641 conf.low conf.high df outcome 1 -0.1285525 0.1485525 198 Y > > # Linear regression using lm_robust and tidy summary > design_1 <- + design + + declare_estimator( + formula = Y ~ Z, + .method = lm_robust, + .summary = tidy, + term = "Z", + inquiry = "ATE", + label = "lm_no_controls" + ) > > run_design(design_1) inquiry estimand term estimator estimate std.error statistic p.value 1 ATE 0.106 Z lm_no_controls 0.01 0.06842381 0.146148 0.8839533 conf.low conf.high df outcome 1 -0.124933 0.144933 198 Y > > # Use glance summary function to view model fit statistics > design_2 <- + design + + declare_estimator(.method = lm_robust, + formula = Y ~ Z, + .summary = glance) > > run_design(design_2) inquiry estimand estimator r.squared adj.r.squared statistic p.value 1 ATE 0.062 estimator 0.00490049 -0.0001252651 0.9750754 0.3246228 df.residual nobs se_type 1 198 200 HC2 > > # Use declare_estimator to implement custom answer strategies > my_estimator <- function(data) { + data.frame(estimate = mean(data$Y)) + } > > design_3 <- + design + + declare_inquiry(Y_bar = mean(Y)) + + declare_estimator(handler = label_estimator(my_estimator), + label = "mean", + inquiry = "Y_bar") > > run_design(design_3) inquiry estimand estimator estimate 1 Y_bar 0.570 mean 0.57 2 ATE 0.136 <NA> NA > > # Use `term` to select particular coefficients > design_4 <- + design + + declare_inquiry(difference_in_cates = mean(Y_Z_1[gender == 1] - Y_Z_0[gender == 1]) - + mean(Y_Z_1[gender == 0] - Y_Z_0[gender == 0])) + + declare_estimator(Y ~ Z * gender, + term = "Z:gender", + inquiry = "difference_in_cates", + .method = lm_robust) > > run_design(design_4) inquiry estimand term estimator estimate std.error 1 difference_in_cates 0.1443609 Z:gender estimator 0.1966622 0.1381796 2 ATE 0.1180000 <NA> <NA> NA NA statistic p.value conf.low conf.high df outcome 1 1.423236 0.1562576 -0.07584754 0.469172 196 Y 2 NA NA NA NA NA <NA> > > # Use glm from base R > design_5 <- + design + + declare_estimator(Y ~ Z + gender, + family = "gaussian", + inquiry = "ATE", + .method = glm) > > run_design(design_5) inquiry estimand estimator term estimate std.error statistic p.value 1 ATE 0.074 estimator Z 0.07 0.07 1 0.3185372 conf.low conf.high 1 -0.06719748 0.2071975 > > # If we use logit, we'll need to estimate the average marginal effect with > # margins::margins. We wrap this up in function we'll pass to model_summary > > library(margins) # for margins Error in library(margins) : there is no package called ‘margins’ Execution halted Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc