8. Benchmarking SDM Package Performance in R
Source:vignettes/articles/runtime-benchmark.Rmd
runtime-benchmark.RmdMotivation
Species distribution models (SDMs) are implemented in several R packages, each providing different workflows, abstractions, and computational strategies. Despite their widespread use, direct and reproducible comparisons of computational performance are rare, largely because differences in data preparation, validation strategies, and algorithm implementations obscure fair comparisons.
This document provides a fully reproducible benchmark comparing the runtime performance of four widely used SDM frameworks:
- biomod2
- sdm
- caretSDM
All packages are evaluated under identical conditions, using the same data, algorithms, and validation structure.
Benchmark design
To ensure fairness and reproducibility, we enforced the following constraints:
- Identical pseudoabsence method
- Identical environmental predictors
- Identical validation method
- Same algorithms across packages (Artificial Neural Network, Classification Tree Analysis, Flexible Discriminant Analysis, Boosted Regression Trees, Multiple Adaptive Regression Splines and Random Forest)
- No parallelization
- Runtime measured using the
benchpackage
Benchmarks included three stages: preprocessing (data formatting), processing (model fitting), and postprocessing (projection). A complete end-to-end benchmark was also conducted. Preprocessing included data formatting and pseudoabsence generation, processing included model fitting with cross-validation and ensembling, and postprocessing included projection to future scenarios.
Packages
library(terra)
library(sf)
library(bench)
library(biomod2)
library(sdm)
library(caretSDM)
library(RSNNS)
library(rpart)
library(mda)
library(gbm)
library(earth)
library(randomForest)Data
Presence–absence data
head(occ)
#> species decimalLongitude decimalLatitude
#> 327 Araucaria angustifolia -4700678 -3065133
#> 405 Araucaria angustifolia -4711827 -3146727
#> 404 Araucaria angustifolia -4711885 -3147170
#> 310 Araucaria angustifolia -4717665 -3142767
#> 49 Araucaria angustifolia -4726011 -3148963
#> 124 Araucaria angustifolia -4727265 -3148517Environmental predictors
bioc
#> stars object with 3 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> current 14.58698 21.19678 298.9147 622.9417 1353.5 2368 1845
#> dimension(s):
#> from to offset delta refsys point values x/y
#> x 747 798 -180 0.1667 WGS 84 FALSE NULL [x]
#> y 670 706 90 -0.1667 WGS 84 FALSE NULL [y]
#> band 1 3 NA NA NA NA bio1 , bio4 , bio12Package-specific benchmark functions
biomod2
# ---------------------------
# biomod2 - Pre-processing
# ---------------------------
prep_biomod2 <- function() {
occ_biomod <- occ
occ_biomod$species <- rep(1, nrow(occ))
env <- terra::rast(stars::st_warp(bioc, crs = 6933))
sc <- list(
scen1 = terra::rast(stars::st_warp(scen[1], crs = 6933)),
scen2 = terra::rast(stars::st_warp(scen[2], crs = 6933)),
scen3 = terra::rast(stars::st_warp(scen[3], crs = 6933)),
scen4 = terra::rast(stars::st_warp(scen[4], crs = 6933))
)
biomod_data <- BIOMOD_FormatingData(
resp.var = occ_biomod$species,
expl.var = env,
resp.xy = occ_biomod[, c("decimalLongitude", "decimalLatitude")],
resp.name = "species",
PA.nb.rep = 1,
PA.nb.absences = nrow(occ_biomod),
PA.strategy = "random"
)
list(data = biomod_data, scen = sc)
}
# ---------------------------
# biomod2 - Processing
# ---------------------------
fit_biomod2 <- function(prep) {
biomod_model <- BIOMOD_Modeling(
prep$data,
models = c("ANN", "CTA", "FDA", "GBM", "MARS", "RF"),
CV.strategy = "kfold",
CV.nb.rep = 1,
CV.k = 5,
metric.eval = c("TSS", "ROC"),
do.progress = FALSE
)
biomod_ensmodel <- BIOMOD_EnsembleModeling(
bm.mod = biomod_model,
models.chosen = 'all',
em.by = 'all',
em.algo = c('EMmean', 'EMwmean', 'EMca'),
metric.select = c('AUCroc'),
metric.select.thresh = c(0.5)
)
list(model = biomod_model,
ensemble = biomod_ensmodel,
scen = prep$scen)
}
# ---------------------------
# biomod2 - Post-processing
# ---------------------------
post_biomod2 <- function(fit) {
biomod_proj <- lapply(names(fit$scen), function(nm) {
BIOMOD_Projection(
bm.mod = fit$model,
proj.name = nm,
new.env = fit$scen[[nm]],
models.chosen = 'all',
overwrite = TRUE,
do.stack = FALSE
)
})
mapply(function(proj, nm) {
BIOMOD_EnsembleForecasting(
bm.em = fit$ensemble,
bm.proj = proj,
proj.name = nm,
models.chosen = 'all',
overwrite = TRUE
)
}, biomod_proj, names(fit$scen))
}
# ---------------------------
# biomod2 - Complete
# ---------------------------
run_biomod2 <- function() {
occ_biomod <- occ
occ_biomod$species <- rep(1, nrow(occ))
env <- terra::rast(stars::st_warp(bioc, crs=6933))
sc <- list(scen1 = terra::rast(stars::st_warp(scen[1], crs=6933)),
scen2 = terra::rast(stars::st_warp(scen[2], crs=6933)),
scen3 = terra::rast(stars::st_warp(scen[3], crs=6933)),
scen4 = terra::rast(stars::st_warp(scen[4], crs=6933)))
biomod_data <- BIOMOD_FormatingData(
resp.var = occ_biomod$species,
expl.var = env,
resp.xy = occ_biomod[, c("decimalLongitude", "decimalLatitude")],
resp.name = "species",
PA.nb.rep = 1,
PA.nb.absences = nrow(occ_biomod),
PA.strategy = "random"
)
biomod_model <- BIOMOD_Modeling(
biomod_data,
models = c("ANN", "CTA", "FDA", "GBM", "MARS", "RF"),
CV.strategy = "kfold",
CV.nb.rep = 1,
CV.k = 5,
metric.eval = c("TSS", "ROC"),
do.progress = FALSE
)
biomod_ensmodel <- BIOMOD_EnsembleModeling(
bm.mod = biomod_model,
models.chosen = 'all',
em.by = 'all',
em.algo = c('EMmean', 'EMwmean', 'EMca'),
metric.select = c('AUCroc'),
metric.select.thresh = c(0.5)
)
biomod_proj <- lapply(names(sc), function(nm) {
BIOMOD_Projection(
bm.mod = biomod_model,
proj.name = nm,
new.env = sc[[nm]],
models.chosen = 'all',
overwrite = TRUE,
do.stack = FALSE
)
})
biomod_ensforecast <- mapply(function(proj, nm) {
BIOMOD_EnsembleForecasting(
bm.em = biomod_ensmodel,
bm.proj = proj,
proj.name = nm,
models.chosen = 'all',
overwrite = TRUE
)
}, biomod_proj, names(scen))
}sdm
# ---------------------------
# sdm - Pre-processing
# ---------------------------
prep_sdm <- function() {
coords <- occ[, c("decimalLongitude", "decimalLatitude")]
names(coords) <- c("x", "y")
env <- terra::rast(stars::st_warp(bioc, crs = 6933))
pres_vals <- terra::extract(env, vect(coords))
pres_vals <- pres_vals[, -1]
pres_data <- cbind(species = 1, pres_vals)
bg <- sdm::background(env, n = nrow(pres_data), method = 'gRandom')
bg$species <- 0
train <- rbind(dplyr::select(bg, -c("x", "y")), pres_data)
sc <- list(
scen1 = terra::rast(stars::st_warp(scen[1], crs = 6933)),
scen2 = terra::rast(stars::st_warp(scen[2], crs = 6933)),
scen3 = terra::rast(stars::st_warp(scen[3], crs = 6933)),
scen4 = terra::rast(stars::st_warp(scen[4], crs = 6933))
)
d <- sdmData(
formula = species ~ bio1 + bio4 + bio12,
train = train
)
list(data = d, scen = sc)
}
# ---------------------------
# sdm - Processing
# ---------------------------
fit_sdm <- function(prep) {
m <- sdm(
formula = species ~ bio1 + bio4 + bio12,
data = prep$data,
methods = c("mlp", "rpart", "fda", "brt", "mars", "rf"),
replication = "cv",
cv.folds = 5
)
list(model = m, scen = prep$scen)
}
# ---------------------------
# biomod2 - Post-processing
# ---------------------------
post_sdm <- function(fit) {
lapply(fit$scen, function(r) {
ensemble(fit$model, newdata = r,
setting = list(method = 'unweighted'))
})
lapply(fit$scen, function(r) {
ensemble(fit$model, newdata = r,
setting = list(method = 'weighted', stat = 'AUC',
expr = 'auc > 0.5'))
})
lapply(fit$scen, function(r) {
ensemble(fit$model, newdata = r,
setting = list(method = 'pa', opt = 2))
})
}
# ---------------------------
# sdm - Complete
# ---------------------------
run_sdm <- function() {
occ_sdm <- occ
coords <- occ_sdm[, c("decimalLongitude", "decimalLatitude")]
names(coords) <- c("x", "y")
presence <- occ_sdm[, c("species", "decimalLongitude", "decimalLatitude")]
names(presence) <- c("species", "x", "y")
env <- terra::rast(stars::st_warp(bioc, crs=6933))
pres_vals <- terra::extract(env, vect(coords))
pres_vals <- pres_vals[, -1]
pres_data <- cbind(presence, pres_vals)
pres_data$species <- rep(1, nrow(pres_data))
sc <- list(scen1 = terra::rast(stars::st_warp(scen[1], crs=6933)),
scen2 = terra::rast(stars::st_warp(scen[2], crs=6933)),
scen3 = terra::rast(stars::st_warp(scen[3], crs=6933)),
scen4 = terra::rast(stars::st_warp(scen[4], crs=6933)))
bg <- sdm::background(env, n=nrow(pres_data), method = 'gRandom')
bg$species <- rep(0, nrow(bg))
pres <- rbind(bg, pres_data)
d <- sdmData(
formula = species ~ bio1 + bio4 + bio12,
train = dplyr::select(pres, -c("x", "y"))
)
m <- sdm(
formula = species ~ bio1 + bio4 + bio12,
data = d,
methods = c("mlp", "rpart", "fda", "brt", "mars", "rf"),
replication = "cv",
cv.folds = 5
)
lapply(sc, function(r) {
ensemble(m, newdata = r,
setting = list(method = 'unweighted'))
})
lapply(sc, function(r) {
ensemble(m, newdata = r,
setting = list(method = 'weighted', stat = 'AUC',
expr = 'auc > 0.5'))
})
lapply(sc, function(r) {
ensemble(m, newdata = r,
setting = list(method = 'pa', opt = 2))
})
}Note: The
sdmpackage does not natively accept externally defined folds. Therefore, although the number of folds is controlled, the exact split structure may differ slightly.
caretSDM
# ---------------------------
# caretSDM - Pre-processing
# ---------------------------
prep_caretSDM <- function() {
sa <- sdm_area(bioc) |>
add_scenarios(scen)
oc <- occurrences_sdm(occ, crs = 6933)
input_sdm(oc, sa) |>
pseudoabsences(method = "random", n_set = 1)
}
# ---------------------------
# caretSDM - Processing
# ---------------------------
fit_caretSDM <- function(prep) {
prep |>
train_sdm(
algo = c("mlp", "rpart", "fda", "gbm", "gcvEarth", "rf"),
tuneLength = 1,
ctrl = caret::trainControl(
method = "cv",
number = 5,
classProbs = TRUE,
returnResamp = "none",
savePredictions = "final",
summaryFunction = summary_sdm
)
)
}
# ---------------------------
# caretSDM - Post-processing
# ---------------------------
post_caretSDM <- function(fit) {
fit |>
predict_sdm(th = 0.5) |>
ensemble_sdm()
}
# ---------------------------
# caretSDM - Complete
# ---------------------------
run_caretSDM <- function() {
sa <- sdm_area(bioc)
oc <- occurrences_sdm(occ, crs = 6933)
i <- input_sdm(oc, sa) |>
pseudoabsences(method = "random", n_set = 1) |>
train_sdm(algo = c("mlp", "rpart", "fda", "gbm", "gcvEarth", "rf"),
tuneLength = 1,
ctrl = caret::trainControl(method = "cv",
number = 5,
classProbs = TRUE,
returnResamp = "none",
savePredictions = "final",
summaryFunction = summary_sdm)) |>
add_scenarios(scen, pred_as_scen = FALSE) |>
predict_sdm(th = 0.5) |>
ensemble_sdm()
}Benchmark execution
Before benchmarking, users are encouraged to run each function once to avoid first-run overhead (e.g. package initialization).
prep_biomod2_res <- prep_biomod2()
prep_sdm_res <- prep_sdm()
prep_caretSDM_res <- prep_caretSDM()
bench_res_prep <- bench::mark(
biomod2 = prep_biomod2(),
sdm = prep_sdm(),
caretSDM = prep_caretSDM(),
iterations = 5,
check = FALSE
)
fit_biomod2_res <- fit_biomod2(prep_biomod2_res)
fit_sdm_res <- fit_sdm(prep_sdm_res)
fit_caretSDM_res <- fit_caretSDM(prep_caretSDM_res)
bench_res_fit <- bench::mark(
biomod2 = fit_biomod2(prep_biomod2_res),
sdm = fit_sdm(prep_sdm_res),
caretSDM = fit_caretSDM(prep_caretSDM_res),
iterations = 5,
check = FALSE
)
post_biomod2(fit_biomod2_res)
post_sdm(fit_sdm_res)
post_caretSDM(fit_caretSDM_res)
bench_res_post <- bench::mark(
biomod2 = post_biomod2(fit_biomod2_res),
sdm = post_sdm(fit_sdm_res),
caretSDM = post_caretSDM(fit_caretSDM_res),
iterations = 5,
check = FALSE
)
bench_res_complete <- bench::mark(
biomod2 = run_biomod2(),
sdm = run_sdm(),
caretSDM = run_caretSDM(),
iterations = 5,
check = FALSE
)Results
bench_res_prep
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 biomod2 164.6ms 169.14ms 5.64 7.75MB 1.13
#> 2 sdm 131.9ms 140.59ms 4.21 6.02MB 0.843
#> 3 caretSDM 1.6s 1.62s 0.584 7.31MB 1.52
bench_res_fit
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 biomod2 7.9s 7.93s 0.125 1016.25MB 0.426
#> 2 sdm 16.7s 16.79s 0.0589 2.18GB 0.295
#> 3 caretSDM 23.4s 26.14s 0.0376 1.22GB 0.835
bench_res_post
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 biomod2 10.26s 10.51s 0.0929 963MB 0.446
#> 2 sdm 18.6s 18.77s 0.0530 579MB 0
#> 3 caretSDM 1.03s 1.04s 0.910 47MB 0.182
bench_res_complete
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 biomod2 18.9s 20.6s 0.0487 1.92GB 0.526
#> 2 sdm 39.1s 40.3s 0.0247 3.02GB 0.0988
#> 3 caretSDM 25.1s 28.8s 0.0346 1.28GB 0.975The table above summarizes the median runtime, iteration rate, and memory allocation for each package under identical conditions.
Interpretation
The benchmark results reveal clear differences in computational performance across the evaluated SDM frameworks, with the magnitude of these differences varying substantially among workflow stages.
Pre-processing
During the preprocessing stage, both biomod2 and sdm completed data preparation rapidly. In contrast, caretSDM required substantially more time, despite still being a very low running time.
This difference primarily reflects the greater amount of internal data structuring performed by caretSDM, including the creation of standardized SDM objects, explicit pseudoabsence management, and preparation of workflow metadata used in later modeling and prediction steps. By contrast, biomod2 and sdm perform less structural preprocessing, relying more directly on raw data objects.
Model fitting
Model fitting is more computationally demanding. Here, biomod2 exhibited the fastest performance, followed by sdm and caretSDM.Memory allocation during this stage also differed substantially across packages. The sdm package required the largest memory allocation, whereas caretSDM and biomod2 used a more close amount, with caretSDM allocating slightly more memory than biomod2.
These differences largely arise from how each framework orchestrates model training. The modeling functions in biomod2 appear to implement relatively efficient internal routines for coordinating algorithms and cross-validation. In contrast, caretSDM relies on the training infrastructure of the caret framework, which provides highly flexible resampling and tuning capabilities but introduces additional overhead during model training.
Post-processing (projection and ensembling)
The largest divergence among packages was observed during the post-processing stage. The sdm package completed projections and ensemble predictions most rapidly, followed by biomod2. In contrast, caretSDM required substantially longer runtimes and allocated considerably more memory.
This increase reflects the design of caretSDM’s prediction pipeline, which explicitly manages multiple prediction objects, scenarios, and ensemble outputs using structured SDM containers. While this approach provides consistent handling of predictions and facilitates downstream analysis, it introduces additional computational overhead compared with the more direct projection implementations used in biomod2 and sdm.
End-to-end workflow
When considering the complete workflow (preprocessing, model fitting, and projection), biomod2 was the fastest framework, followed by the sdm package and finally the caretSDM package. Memory usage followed a similar pattern, with caretSDM allocating the largest total memory, followed by sdm and biomod2.
Overall implications
These results highlight that differences among SDM frameworks arise not only from algorithm implementation but also from workflow architecture. Packages such as biomod2 and sdm prioritize computational efficiency by performing fewer intermediate abstractions and operating closer to the underlying modeling functions. In contrast, caretSDM emphasizes workflow standardization, object consistency, and integration with a general machine-learning framework, which introduces additional computational overhead but may provide advantages in reproducibility, extensibility, and workflow transparency. Moreover, caretSDM also needed substantial less lines of code to perform the same tasks, while also using only functions from its own package. This may be an advantage for users who prefer a more streamlined workflow or want to spend more coding time in other stages of the modeling process, such as post-processing.
During preprocessing, this benchmark avoided the use of multiple sets of pseudoabsences to ensure that all packages were evaluated under identical conditions. However, in practice, users may choose to generate multiple sets of pseudoabsences to improve model robustness, which would likely amplify the observed differences in preprocessing time among packages. This approach was intentional due to the lack of easy way to perform this task in sdm package, which would require users to implement custom code to generate multiple sets of pseudoabsences and manage them across modeling iterations. This would probably reflect more a difference in user coding ability than in package performance, which is not the focus of this benchmark.
The use of multiple sets of pseudoabsences would likely amplify the observed differences in model fitting time among packages in the processing step. In the same way, hyperparameter tuning was let out of this benchmark to ensure that all packages were evaluated under identical conditions. However, users may choose to perform tuning to optimize model performance particularly when using caretSDM, which provides more extensive and auto-tuning capabilities. As previously stated, to include these methods in other packages would probably reflect more a difference in user coding ability than in package performance, which is not the focus of this benchmark.
Finally, in postprocessing, the differences between packages reflect the design choices made by each framework regarding how projections and ensemble predictions are managed. While the more structured approach of caretSDM provides advantages in terms of workflow consistency and downstream analysis, it introduces additional computational overhead compared with the more direct implementations used in biomod2 and sdm. Moreover, biomod2 excels in time and memory management, once it has a strong output flow, that is, it does not create many intermediate objects during the workflow, which may be an advantage for users with limited computational resources or those who prioritize efficiency.
Reproducibility
This benchmark is fully reproducible and can be executed on any system with the required packages installed. Users are encouraged to rerun the analysis on their own hardware to assess absolute performance differences.
Limitations
- No parallel processing was used
- Performance may vary with dataset size, predictor dimensionality, and hardware
- If you want to see your package here or if you think I am missing some coding or function from sdm and/or biomod2, please contact me on luizesser@gmail.com.
Session information
sessionInfo()
#> R version 4.5.3 (2026-03-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] splines stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] caret_7.0-1 lattice_0.22-9 ggplot2_4.0.2
#> [4] kernlab_0.9-33 glmnet_4.1-10 Matrix_1.7-4
#> [7] dismo_1.3-16 raster_3.6-32 sp_2.2-1
#> [10] RSNNS_0.4-18 Rcpp_1.1.1 caretSDM_1.7
#> [13] sdm_1.2-59 xgboost_3.2.1.1 randomForest_4.7-1.2
#> [16] maxnet_0.1.4 earth_5.3.5 plotmo_3.7.0
#> [19] plotrix_3.8-14 Formula_1.2-5 gbm_2.2.3
#> [22] mgcv_1.9-4 nlme_3.1-168 gam_1.22-7
#> [25] foreach_1.5.2 mda_0.5-5 class_7.3-23
#> [28] cito_1.1 rpart_4.1.24 nnet_7.3-20
#> [31] biomod2_4.3-4-5 bench_1.1.4 sf_1.1-0
#> [34] terra_1.9-1
#>
#> loaded via a namespace (and not attached):
#> [1] blockCV_3.2-0 rnaturalearth_1.2.0 tibble_3.3.1
#> [4] R.oo_1.27.1 hardhat_1.4.2 pROC_1.19.0.1
#> [7] lifecycle_1.0.5 httr2_1.2.2 ecospat_4.1.3
#> [10] usdm_2.1-7 globals_0.19.1 processx_3.8.6
#> [13] MASS_7.3-65 crosstalk_1.2.2 backports_1.5.0
#> [16] magrittr_2.0.4 sass_0.4.10 rmarkdown_2.30
#> [19] jquerylib_0.1.4 yaml_2.3.12 otel_0.2.0
#> [22] DBI_1.3.0 RColorBrewer_1.1-3 lubridate_1.9.5
#> [25] abind_1.4-8 Rtsne_0.17 purrr_1.2.1
#> [28] R.utils_2.13.0 coro_1.1.0 rappdirs_0.3.4
#> [31] ipred_0.9-15 torch_0.16.3 satellite_1.0.6
#> [34] lava_1.8.2 listenv_0.10.1 units_1.0-1
#> [37] parallelly_1.46.1 pkgdown_2.2.0 PresenceAbsence_1.1.11
#> [40] codetools_0.2-20 profmem_0.7.0 xml2_1.5.2
#> [43] shape_1.4.6.1 tidyselect_1.2.1 farver_2.1.2
#> [46] stats4_4.5.3 base64enc_0.1-6 jsonlite_2.0.0
#> [49] e1071_1.7-17 progressr_0.18.0 survival_3.8-6
#> [52] ggspatial_1.1.10 iterators_1.0.14 systemfonts_1.3.2
#> [55] tools_4.5.3 ragg_1.5.1 stringdist_0.9.17
#> [58] glue_1.8.0 prodlim_2026.03.11 xfun_0.57
#> [61] checkCLI_1.0 dplyr_1.2.0 withr_3.0.2
#> [64] fastmap_1.2.0 callr_3.7.6 digest_0.6.39
#> [67] CoordinateCleaner_3.0.1 timechange_0.4.0 R6_2.6.1
#> [70] wk_0.9.5 textshaping_1.0.5 gtools_3.9.5
#> [73] R.methodsS3_1.8.2 utf8_1.2.6 tidyr_1.3.2
#> [76] generics_0.1.4 data.table_1.18.2.1 recipes_1.3.1
#> [79] httr_1.4.8 htmlwidgets_1.6.4 whisker_0.4.1
#> [82] ModelMetrics_1.2.2.2 pkgconfig_2.0.3 gtable_0.3.6
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