Error(s) in re-building vignettes:
  ...
--- re-building 'introduction_to_vimp.Rmd' using rmarkdown
Chunk learner-lib-small:
  Loading required package: nnls 

library("SuperLearner")
## load specific algorithms
library("gam")
library("xgboost")
SL.xgboost1 <- function(..., max_depth = 1, ntree = 500, shrinkage = 0.1){
  SL.xgboost(..., max_depth = max_depth, ntree = ntree, shrinkage = shrinkage)
}
Chunk learner-lib-small:
  Super Learner 

library("SuperLearner")
## load specific algorithms
library("gam")
library("xgboost")
SL.xgboost1 <- function(..., max_depth = 1, ntree = 500, shrinkage = 0.1){
  SL.xgboost(..., max_depth = max_depth, ntree = ntree, shrinkage = shrinkage)
}
Chunk learner-lib-small:
  Version: 2.0-26 

library("SuperLearner")
## load specific algorithms
library("gam")
library("xgboost")
SL.xgboost1 <- function(..., max_depth = 1, ntree = 500, shrinkage = 0.1){
  SL.xgboost(..., max_depth = max_depth, ntree = ntree, shrinkage = shrinkage)
}
Chunk learner-lib-small:
  Package created on 2019-10-27 

library("SuperLearner")
## load specific algorithms
library("gam")
library("xgboost")
SL.xgboost1 <- function(..., max_depth = 1, ntree = 500, shrinkage = 0.1){
  SL.xgboost(..., max_depth = max_depth, ntree = ntree, shrinkage = shrinkage)
}
Chunk learner-lib-small:
  Loading required package: splines 

library("SuperLearner")
## load specific algorithms
library("gam")
library("xgboost")
SL.xgboost1 <- function(..., max_depth = 1, ntree = 500, shrinkage = 0.1){
  SL.xgboost(..., max_depth = max_depth, ntree = ntree, shrinkage = shrinkage)
}
Chunk learner-lib-small:
  Loading required package: foreach 

library("SuperLearner")
## load specific algorithms
library("gam")
library("xgboost")
SL.xgboost1 <- function(..., max_depth = 1, ntree = 500, shrinkage = 0.1){
  SL.xgboost(..., max_depth = max_depth, ntree = ntree, shrinkage = shrinkage)
}
Chunk learner-lib-small:
  Loaded gam 1.16.1 

library("SuperLearner")
## load specific algorithms
library("gam")
library("xgboost")
SL.xgboost1 <- function(..., max_depth = 1, ntree = 500, shrinkage = 0.1){
  SL.xgboost(..., max_depth = max_depth, ntree = ntree, shrinkage = shrinkage)
}
Chunk est-heart-regressions-lm:
  
Attaching package: 'dplyr' 

## estimate the full conditional mean using linear regression
full_mod <- lm(chd ~ ., data = subset(heart, heart_folds == 1))
full_fit <- predict(full_mod)

## estimate the reduced conditional means for each of the individual variables
X <- as.matrix(heart[, -dim(heart)[2]])[heart_folds == 2, ] # remove the outcome for the predictor matrix
red_mod_sbp <- lm(full_fit ~ X[,-1])
red_fit_sbp <- predict(red_mod_sbp)
red_mod_tob <- lm(full_fit ~ X[,-2])
red_fit_tob <- predict(red_mod_tob)
red_mod_ldl <- lm(full_fit ~ X[,-3])
red_fit_ldl <- predict(red_mod_ldl)
red_mod_adi <- lm(full_fit ~ X[,-4])
red_fit_adi <- predict(red_mod_adi)
red_mod_fam <- lm(full_fit ~ X[,-5])
red_fit_fam <- predict(red_mod_fam)
red_mod_tpa <- lm(full_fit ~ X[, -6])
red_fit_tpa <- predict(red_mod_tpa)
red_mod_obe <- lm(full_fit ~ X[,-7])
red_fit_obe <- predict(red_mod_obe)
red_mod_alc <- lm(full_fit ~ X[, -8])
red_fit_alc <- predict(red_mod_alc)
red_mod_age <- lm(full_fit ~ X[,-9])
red_fit_age <- predict(red_mod_age)

## load the library
library("vimp")
library("dplyr")

## plug these into vim
lm_vim_sbp <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_sbp, indx = 1, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tob <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tob, indx = 2, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_ldl <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_ldl, indx = 3, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_adi <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_adi, indx = 4, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_fam <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_fam, indx = 5, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tpa <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tpa, indx = 6, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_obe <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_obe, indx = 7, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_alc <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_alc, indx = 8, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_age <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_age, indx = 9, run_regression = FALSE, type = "r_squared", folds = heart_folds)

## make a table with the estimates using the merge_vim() function
lm_mat <- merge_vim(lm_vim_sbp, lm_vim_tob, lm_vim_ldl, lm_vim_adi,
                lm_vim_fam, lm_vim_tpa, lm_vim_obe, lm_vim_alc, lm_vim_age)
## print out the matrix
lm_mat
Chunk est-heart-regressions-lm:
  The following object is masked from 'package:xgboost':

    slice 

## estimate the full conditional mean using linear regression
full_mod <- lm(chd ~ ., data = subset(heart, heart_folds == 1))
full_fit <- predict(full_mod)

## estimate the reduced conditional means for each of the individual variables
X <- as.matrix(heart[, -dim(heart)[2]])[heart_folds == 2, ] # remove the outcome for the predictor matrix
red_mod_sbp <- lm(full_fit ~ X[,-1])
red_fit_sbp <- predict(red_mod_sbp)
red_mod_tob <- lm(full_fit ~ X[,-2])
red_fit_tob <- predict(red_mod_tob)
red_mod_ldl <- lm(full_fit ~ X[,-3])
red_fit_ldl <- predict(red_mod_ldl)
red_mod_adi <- lm(full_fit ~ X[,-4])
red_fit_adi <- predict(red_mod_adi)
red_mod_fam <- lm(full_fit ~ X[,-5])
red_fit_fam <- predict(red_mod_fam)
red_mod_tpa <- lm(full_fit ~ X[, -6])
red_fit_tpa <- predict(red_mod_tpa)
red_mod_obe <- lm(full_fit ~ X[,-7])
red_fit_obe <- predict(red_mod_obe)
red_mod_alc <- lm(full_fit ~ X[, -8])
red_fit_alc <- predict(red_mod_alc)
red_mod_age <- lm(full_fit ~ X[,-9])
red_fit_age <- predict(red_mod_age)

## load the library
library("vimp")
library("dplyr")

## plug these into vim
lm_vim_sbp <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_sbp, indx = 1, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tob <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tob, indx = 2, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_ldl <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_ldl, indx = 3, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_adi <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_adi, indx = 4, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_fam <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_fam, indx = 5, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tpa <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tpa, indx = 6, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_obe <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_obe, indx = 7, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_alc <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_alc, indx = 8, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_age <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_age, indx = 9, run_regression = FALSE, type = "r_squared", folds = heart_folds)

## make a table with the estimates using the merge_vim() function
lm_mat <- merge_vim(lm_vim_sbp, lm_vim_tob, lm_vim_ldl, lm_vim_adi,
                lm_vim_fam, lm_vim_tpa, lm_vim_obe, lm_vim_alc, lm_vim_age)
## print out the matrix
lm_mat
Chunk est-heart-regressions-lm:
  The following objects are masked from 'package:stats':

    filter, lag 

## estimate the full conditional mean using linear regression
full_mod <- lm(chd ~ ., data = subset(heart, heart_folds == 1))
full_fit <- predict(full_mod)

## estimate the reduced conditional means for each of the individual variables
X <- as.matrix(heart[, -dim(heart)[2]])[heart_folds == 2, ] # remove the outcome for the predictor matrix
red_mod_sbp <- lm(full_fit ~ X[,-1])
red_fit_sbp <- predict(red_mod_sbp)
red_mod_tob <- lm(full_fit ~ X[,-2])
red_fit_tob <- predict(red_mod_tob)
red_mod_ldl <- lm(full_fit ~ X[,-3])
red_fit_ldl <- predict(red_mod_ldl)
red_mod_adi <- lm(full_fit ~ X[,-4])
red_fit_adi <- predict(red_mod_adi)
red_mod_fam <- lm(full_fit ~ X[,-5])
red_fit_fam <- predict(red_mod_fam)
red_mod_tpa <- lm(full_fit ~ X[, -6])
red_fit_tpa <- predict(red_mod_tpa)
red_mod_obe <- lm(full_fit ~ X[,-7])
red_fit_obe <- predict(red_mod_obe)
red_mod_alc <- lm(full_fit ~ X[, -8])
red_fit_alc <- predict(red_mod_alc)
red_mod_age <- lm(full_fit ~ X[,-9])
red_fit_age <- predict(red_mod_age)

## load the library
library("vimp")
library("dplyr")

## plug these into vim
lm_vim_sbp <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_sbp, indx = 1, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tob <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tob, indx = 2, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_ldl <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_ldl, indx = 3, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_adi <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_adi, indx = 4, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_fam <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_fam, indx = 5, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tpa <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tpa, indx = 6, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_obe <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_obe, indx = 7, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_alc <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_alc, indx = 8, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_age <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_age, indx = 9, run_regression = FALSE, type = "r_squared", folds = heart_folds)

## make a table with the estimates using the merge_vim() function
lm_mat <- merge_vim(lm_vim_sbp, lm_vim_tob, lm_vim_ldl, lm_vim_adi,
                lm_vim_fam, lm_vim_tpa, lm_vim_obe, lm_vim_alc, lm_vim_age)
## print out the matrix
lm_mat
Chunk est-heart-regressions-lm:
  The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union 

## estimate the full conditional mean using linear regression
full_mod <- lm(chd ~ ., data = subset(heart, heart_folds == 1))
full_fit <- predict(full_mod)

## estimate the reduced conditional means for each of the individual variables
X <- as.matrix(heart[, -dim(heart)[2]])[heart_folds == 2, ] # remove the outcome for the predictor matrix
red_mod_sbp <- lm(full_fit ~ X[,-1])
red_fit_sbp <- predict(red_mod_sbp)
red_mod_tob <- lm(full_fit ~ X[,-2])
red_fit_tob <- predict(red_mod_tob)
red_mod_ldl <- lm(full_fit ~ X[,-3])
red_fit_ldl <- predict(red_mod_ldl)
red_mod_adi <- lm(full_fit ~ X[,-4])
red_fit_adi <- predict(red_mod_adi)
red_mod_fam <- lm(full_fit ~ X[,-5])
red_fit_fam <- predict(red_mod_fam)
red_mod_tpa <- lm(full_fit ~ X[, -6])
red_fit_tpa <- predict(red_mod_tpa)
red_mod_obe <- lm(full_fit ~ X[,-7])
red_fit_obe <- predict(red_mod_obe)
red_mod_alc <- lm(full_fit ~ X[, -8])
red_fit_alc <- predict(red_mod_alc)
red_mod_age <- lm(full_fit ~ X[,-9])
red_fit_age <- predict(red_mod_age)

## load the library
library("vimp")
library("dplyr")

## plug these into vim
lm_vim_sbp <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_sbp, indx = 1, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tob <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tob, indx = 2, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_ldl <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_ldl, indx = 3, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_adi <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_adi, indx = 4, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_fam <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_fam, indx = 5, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_tpa <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_tpa, indx = 6, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_obe <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_obe, indx = 7, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_alc <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_alc, indx = 8, run_regression = FALSE, type = "r_squared", folds = heart_folds)
lm_vim_age <- vim(Y = heart$chd, f1 = full_fit, f2 = red_fit_age, indx = 9, run_regression = FALSE, type = "r_squared", folds = heart_folds)

## make a table with the estimates using the merge_vim() function
lm_mat <- merge_vim(lm_vim_sbp, lm_vim_tob, lm_vim_ldl, lm_vim_adi,
                lm_vim_fam, lm_vim_tpa, lm_vim_obe, lm_vim_alc, lm_vim_age)
## print out the matrix
lm_mat
Chunk heart-vim:
  
******************************************************** 

library("dplyr")
library("tibble")
library("ggplot2")
library("cowplot")
theme_set(theme_cowplot())
library("forcats")
## combine the objects together
ests <- merge_vim(sbp_vim, tob_vim, ldl_vim, adi_vim,
                fam_vim, tpa_vim, obe_vim, alc_vim, age_vim)

get_nm <- function(s) {
  if (s == 1) {
    return("Sys. blood press.")
  } else if (s == 2) {
    return("Tobacco consump.")
  } else if (s == 3) {
    return("LDL cholesterol")
  } else if (s == 4) {
    return("Adiposity")
  } else if (s == 5) {
    return("Family history")
  } else if (s == 6) {
    return("Type A behavior")
  } else if (s == 7) {
    return("Obesity")
  } else if (s == 8) {
    return("Alcohol consump.")
  } else if (s == 9) {
    return("Age")
  }
}
get_nms <- function(ests) {
  return(apply(matrix(as.numeric(ests$s)), 1, get_nm))
}

## plot
ests$mat %>%
  arrange(desc(est)) %>%
  mutate(ord_group = forcats::fct_reorder(get_nms(ests$mat), est)) %>%
  ggplot(aes(x = est, y = ord_group)) +
  geom_point() +
  geom_errorbarh(aes(xmin = cil, xmax = ciu)) +
  xlab(expression(paste("Variable importance estimates: ", R^2, sep = ""))) +
  ylab("") +
  ggtitle("Variable importance estimates for individual features in the South African heart disease study data")
Chunk heart-vim:
  Note: As of version 1.0.0, cowplot does not change the 

library("dplyr")
library("tibble")
library("ggplot2")
library("cowplot")
theme_set(theme_cowplot())
library("forcats")
## combine the objects together
ests <- merge_vim(sbp_vim, tob_vim, ldl_vim, adi_vim,
                fam_vim, tpa_vim, obe_vim, alc_vim, age_vim)

get_nm <- function(s) {
  if (s == 1) {
    return("Sys. blood press.")
  } else if (s == 2) {
    return("Tobacco consump.")
  } else if (s == 3) {
    return("LDL cholesterol")
  } else if (s == 4) {
    return("Adiposity")
  } else if (s == 5) {
    return("Family history")
  } else if (s == 6) {
    return("Type A behavior")
  } else if (s == 7) {
    return("Obesity")
  } else if (s == 8) {
    return("Alcohol consump.")
  } else if (s == 9) {
    return("Age")
  }
}
get_nms <- function(ests) {
  return(apply(matrix(as.numeric(ests$s)), 1, get_nm))
}

## plot
ests$mat %>%
  arrange(desc(est)) %>%
  mutate(ord_group = forcats::fct_reorder(get_nms(ests$mat), est)) %>%
  ggplot(aes(x = est, y = ord_group)) +
  geom_point() +
  geom_errorbarh(aes(xmin = cil, xmax = ciu)) +
  xlab(expression(paste("Variable importance estimates: ", R^2, sep = ""))) +
  ylab("") +
  ggtitle("Variable importance estimates for individual features in the South African heart disease study data")
Chunk heart-vim:
    default ggplot2 theme anymore. To recover the previous 

library("dplyr")
library("tibble")
library("ggplot2")
library("cowplot")
theme_set(theme_cowplot())
library("forcats")
## combine the objects together
ests <- merge_vim(sbp_vim, tob_vim, ldl_vim, adi_vim,
                fam_vim, tpa_vim, obe_vim, alc_vim, age_vim)

get_nm <- function(s) {
  if (s == 1) {
    return("Sys. blood press.")
  } else if (s == 2) {
    return("Tobacco consump.")
  } else if (s == 3) {
    return("LDL cholesterol")
  } else if (s == 4) {
    return("Adiposity")
  } else if (s == 5) {
    return("Family history")
  } else if (s == 6) {
    return("Type A behavior")
  } else if (s == 7) {
    return("Obesity")
  } else if (s == 8) {
    return("Alcohol consump.")
  } else if (s == 9) {
    return("Age")
  }
}
get_nms <- function(ests) {
  return(apply(matrix(as.numeric(ests$s)), 1, get_nm))
}

## plot
ests$mat %>%
  arrange(desc(est)) %>%
  mutate(ord_group = forcats::fct_reorder(get_nms(ests$mat), est)) %>%
  ggplot(aes(x = est, y = ord_group)) +
  geom_point() +
  geom_errorbarh(aes(xmin = cil, xmax = ciu)) +
  xlab(expression(paste("Variable importance estimates: ", R^2, sep = ""))) +
  ylab("") +
  ggtitle("Variable importance estimates for individual features in the South African heart disease study data")
Chunk heart-vim:
    behavior, execute:
  theme_set(theme_cowplot()) 

library("dplyr")
library("tibble")
library("ggplot2")
library("cowplot")
theme_set(theme_cowplot())
library("forcats")
## combine the objects together
ests <- merge_vim(sbp_vim, tob_vim, ldl_vim, adi_vim,
                fam_vim, tpa_vim, obe_vim, alc_vim, age_vim)

get_nm <- function(s) {
  if (s == 1) {
    return("Sys. blood press.")
  } else if (s == 2) {
    return("Tobacco consump.")
  } else if (s == 3) {
    return("LDL cholesterol")
  } else if (s == 4) {
    return("Adiposity")
  } else if (s == 5) {
    return("Family history")
  } else if (s == 6) {
    return("Type A behavior")
  } else if (s == 7) {
    return("Obesity")
  } else if (s == 8) {
    return("Alcohol consump.")
  } else if (s == 9) {
    return("Age")
  }
}
get_nms <- function(ests) {
  return(apply(matrix(as.numeric(ests$s)), 1, get_nm))
}

## plot
ests$mat %>%
  arrange(desc(est)) %>%
  mutate(ord_group = forcats::fct_reorder(get_nms(ests$mat), est)) %>%
  ggplot(aes(x = est, y = ord_group)) +
  geom_point() +
  geom_errorbarh(aes(xmin = cil, xmax = ciu)) +
  xlab(expression(paste("Variable importance estimates: ", R^2, sep = ""))) +
  ylab("") +
  ggtitle("Variable importance estimates for individual features in the South African heart disease study data")
Chunk heart-vim:
  ******************************************************** 

library("dplyr")
library("tibble")
library("ggplot2")
library("cowplot")
theme_set(theme_cowplot())
library("forcats")
## combine the objects together
ests <- merge_vim(sbp_vim, tob_vim, ldl_vim, adi_vim,
                fam_vim, tpa_vim, obe_vim, alc_vim, age_vim)

get_nm <- function(s) {
  if (s == 1) {
    return("Sys. blood press.")
  } else if (s == 2) {
    return("Tobacco consump.")
  } else if (s == 3) {
    return("LDL cholesterol")
  } else if (s == 4) {
    return("Adiposity")
  } else if (s == 5) {
    return("Family history")
  } else if (s == 6) {
    return("Type A behavior")
  } else if (s == 7) {
    return("Obesity")
  } else if (s == 8) {
    return("Alcohol consump.")
  } else if (s == 9) {
    return("Age")
  }
}
get_nms <- function(ests) {
  return(apply(matrix(as.numeric(ests$s)), 1, get_nm))
}

## plot
ests$mat %>%
  arrange(desc(est)) %>%
  mutate(ord_group = forcats::fct_reorder(get_nms(ests$mat), est)) %>%
  ggplot(aes(x = est, y = ord_group)) +
  geom_point() +
  geom_errorbarh(aes(xmin = cil, xmax = ciu)) +
  xlab(expression(paste("Variable importance estimates: ", R^2, sep = ""))) +
  ylab("") +
  ggtitle("Variable importance estimates for individual features in the South African heart disease study data")

Number of messages:
message 
     16 
pandoc-citeproc: Error in $: Incompatible API versions: encoded with [1,20] but attempted to decode with [1,17,5,4].
CallStack (from HasCallStack):
  error, called at ./Text/Pandoc/JSON.hs:111:48 in pandoc-types-1.17.5.4-5tHZ3B61A58JaKOMxwGQR4:Text.Pandoc.JSON
Error running filter /usr/bin/pandoc-citeproc:
Filter returned error status 1
Error: processing vignette 'introduction_to_vimp.Rmd' failed with diagnostics:
pandoc document conversion failed with error 83
--- failed re-building 'introduction_to_vimp.Rmd'

SUMMARY: processing the following file failed:
  'introduction_to_vimp.Rmd'

Error: Vignette re-building failed.
Execution halted