Error(s) in re-building vignettes:
--- re-building 'introduction-to-vimp.Rmd' using rmarkdown
Quitting from lines 156-169 (introduction-to-vimp.Rmd) 
Error: processing vignette 'introduction-to-vimp.Rmd' failed with diagnostics:
(subscript) logical subscript too long
--- failed re-building 'introduction-to-vimp.Rmd'

--- re-building 'ipcw-vim.Rmd' using rmarkdown
Chunk missing-y-vim:
  vimp version 2.3.1: Perform Inference on Algorithm-Agnostic Variable Importance 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Loading required package: nnls 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Loading required package: gam 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Loading required package: splines 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Loading required package: foreach 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Loaded gam 1.22 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Super Learner 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Version: 2.0-28 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Package created on 2021-05-04 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk ipw-vim:
  Loading required package: quadprog 

library("vimp")
library("SuperLearner")
# estimate the probability of being included only in the first phase sample
ipc_weights <- 1 / predict(glm(C ~ y + V1, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V), method = "method.CC_LS")
Chunk missing-y-vim:
  Warning: All algorithms have zero weight 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Warning: All metalearner coefficients are zero, predictions will all be equal to
0 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))
Chunk missing-y-vim:
  Warning: All metalearner coefficients are zero, predictions will all be equal to
0 

library("vimp")
library("SuperLearner")
# estimate the probability of missing outcome
ipc_weights <- 1 / predict(glm(C ~ V1 + V2, family = "binomial", data = full_df),
                           type = "response")

# set up the SL
learners <- c("SL.glm", "SL.mean")
V <- 2

# estimate vim for X2
set.seed(1234)
est <- vim(Y = obs_y, X = x_df, indx = 2, type = "r_squared", run_regression = TRUE,
           SL.library = learners, alpha = 0.05, delta = 0, C = C, Z = c("Y", "1"),
           ipc_weights = ipc_weights, cvControl = list(V = V))

Number of messages:
message warning 
     10       3 
--- finished re-building 'ipcw-vim.Rmd'

--- re-building 'precomputed-regressions.Rmd' using rmarkdown
--- finished re-building 'precomputed-regressions.Rmd'

--- re-building 'types-of-vims.Rmd' using rmarkdown
Quitting from lines 52-57 (types-of-vims.Rmd) 
Error: processing vignette 'types-of-vims.Rmd' failed with diagnostics:
'predictions' contains NA.
--- failed re-building 'types-of-vims.Rmd'

SUMMARY: processing the following files failed:
  'introduction-to-vimp.Rmd' 'types-of-vims.Rmd'

Error: Vignette re-building failed.
Execution halted