Last updated: 2022-07-29
Checks: 7 0
Knit directory: fitnessGWAS/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20180914) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version f983779. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rapp.history
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: .httr-oauth
Ignored: .pversion
Ignored: analysis/.DS_Store
Ignored: analysis/correlations_SNP_effects_cache/
Ignored: code/.DS_Store
Ignored: code/Drosophila_GWAS.Rmd
Ignored: data/.DS_Store
Ignored: data/derived/
Ignored: data/input/.DS_Store
Ignored: data/input/.pversion
Ignored: data/input/dgrp.fb557.annot.txt
Ignored: data/input/dgrp2.bed
Ignored: data/input/dgrp2.bim
Ignored: data/input/dgrp2.fam
Ignored: data/input/huang_transcriptome/
Ignored: figures/.DS_Store
Untracked files:
Untracked: big_model.rds
Untracked: code/main_paper_figures.Rmd
Untracked: code/main_paper_figures.docx
Untracked: code/pdf_supp_material.Rmd
Untracked: code/pdf_supp_material.pdf
Untracked: code/quant_gen_1.R
Untracked: code/~$in_paper_figures.docx
Untracked: data/input/genomic_relatedness_matrix.rds
Untracked: figures/fig3_boyle_plot.pdf
Untracked: figures/fig4_quartiles_plot.pdf
Untracked: figures/fig5_antagonism_ratios.pdf
Unstaged changes:
Modified: .gitignore
Modified: analysis/index.Rmd
Modified: analysis/plot_models_variant_effects.Rmd
Deleted: figures/GWAS_mixture_proportions.rds
Deleted: figures/GWAS_stats_figure.pdf
Deleted: figures/TWAS_mixture_proportions.rds
Deleted: figures/TWAS_stats_figure.pdf
Deleted: figures/antagonism_ratios.pdf
Deleted: figures/boyle_plot.pdf
Deleted: figures/boyle_plot.png
Deleted: figures/composite_mixture_figure.pdf
Deleted: figures/eff_size_histos.pdf
Modified: figures/fig2_SNPs_manhattan_plot.png
Modified: figures/fig4_quartiles_plot.png
Modified: figures/fig6_models.pdf
Deleted: figures/stats_figure.pdf
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/gcta_quant_genetics.Rmd)
and HTML (docs/gcta_quant_genetics.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f983779 | lukeholman | 2022-07-29 | wflow_publish("analysis/gcta_quant_genetics.Rmd") |
| html | 7449a90 | lukeholman | 2021-10-01 | Build site. |
| html | 8d14298 | lukeholman | 2021-09-26 | Build site. |
| Rmd | af15dd6 | lukeholman | 2021-09-26 | Commit Sept 2021 |
library(tidyverse)
library(glue)
library(kableExtra)
library(brms)
gcta <- file.path(getwd(), "code/gcta64")
options(readr.show_col_types = FALSE)
# helper function to pass commands to the terminal
# Note that we set `intern = TRUE`, and pass the result of `system()` to `cat()`,
# ensuring that the Terminal output will be printed in this knitr report.
run_command <- function(shell_command, wd = getwd(), path = ""){
cat(system(glue("cd ", wd, path, "\n", shell_command), intern = TRUE), sep = '\n')
}
# Load the line mean fitness values calculated by brms
line_means <- read_csv("data/derived/predicted_line_means.csv") %>%
mutate(line = factor(str_remove_all(line, "_"))) %>%
filter(!is.na(male.fitness.early)) %>%
mutate(`Female fitness early` = female.fitness.early,
`Female fitness late` = female.fitness.late,
`Male fitness early` = male.fitness.early,
`Male fitness late` = male.fitness.late) %>%
rename(female_fitness_early = female.fitness.early,
female_fitness_late = female.fitness.late,
male_fitness_early = male.fitness.early,
male_fitness_late = male.fitness.late)The table shows the posterior estimate of the intra-class correlation
coefficient (ICC, which measures the proportion of variance within and
between lines), using the models in the script
get_predicted_line_means.Rmd to adjust for block effects
(and one covariate in the male late-life fitness analysis). ICC was
calculated using ICC::ICCbare. This value approximates the
broad-sense heritability; fitness appears highly heritable, except for
male late-life fitness.
read.csv('data/derived/ICC.csv') %>%
select(Fitness_trait, Estimate, Est.Error, Q2.5, Q97.5) %>%
rename(`Fitness trait` = Fitness_trait, `Est. Error` = Est.Error, `Lower 95% CI` = Q2.5, `Upper 95% CI` = Q97.5) %>%
kable(digits = 2) %>% kable_styling(full_width = FALSE)| Fitness trait | Estimate | Est. Error | Lower 95% CI | Upper 95% CI |
|---|---|---|---|---|
| Female early-life fitness | 0.63 | 0.01 | 0.61 | 0.64 |
| Female late-life fitness | 0.56 | 0.01 | 0.54 | 0.58 |
| Male early-life fitness | 0.45 | 0.01 | 0.43 | 0.46 |
| Male late-life fitness | 0.14 | 0.01 | 0.12 | 0.15 |
line_mean_corrs <- line_means %>% select(contains(" ")) %>% cor() %>% as.data.frame()
saveRDS(line_mean_corrs, "data/derived/line_mean_corrs.rds")
line_mean_corrs %>% kable(digits =3) %>% kable_styling()| Female fitness early | Female fitness late | Male fitness early | Male fitness late | |
|---|---|---|---|---|
| Female fitness early | 1.000 | 0.766 | 0.228 | 0.172 |
| Female fitness late | 0.766 | 1.000 | 0.317 | 0.317 |
| Male fitness early | 0.228 | 0.317 | 1.000 | 0.864 |
| Male fitness late | 0.172 | 0.317 | 0.864 | 1.000 |
gcta64 to estimate the genetic variances and
heritabilities for each fitness traitThe below uses the gcta64 command --reml to
estimate the genetic variance (V(G)), i.e. the variance
explained by the GRM), environmental variance (V(e)), their
sum (Vp), and the so-called “SNP heritability”
(V(G)/Vp). Note that the genetic variance is very low for
male late-life fitness. This is a bit puzzling because the same is not
true for male early-life fitness, which is strongly correlated across
lines with male late-life fitness.
do_univariate_greml <- function(trait1){
# First make 'focal_data', a 2-column data frame with the line and the focal phenotype value
focal_data <- line_means %>%
select(line, !! trait1)
names(focal_data)[2] <- c("focal_pheno")
# Write a file with the line and phenotype data called phenotype.txt, for gcta64
pheno_data <- focal_data %>%
# filter(!(line %in% lines_to_prune)) %>%
mutate(line_copy = line) %>%
select(line, line_copy, focal_pheno) %>%
as.matrix()
pheno_data %>%
write.table(row.names = FALSE, col.names = FALSE,
file = "data/derived/phenotype.txt", quote = FALSE)
console_output <- suppressWarnings(capture.output(
run_command(glue("{gcta} --reml --reml-maxit 10000 --grm gcta_GRM --pheno phenotype.txt --out delete_me"),
path = "/data/derived/")))
errors <- console_output[str_detect(console_output, "[Ee]rror")]
if(length(errors) == 0) errors <- NA
if(length(errors) > 1) errors <- paste(errors, sep = "; ")
rbind(read.delim("data/derived/delete_me.hsq", sep = "\t") ,
data.frame(Source = "Errors", Variance = errors, SE = NA)) %>%
mutate(Trait = trait1) %>% select(Trait, everything())
}
univariate_table <- bind_rows(do_univariate_greml("Female fitness early"),
do_univariate_greml("Female fitness late"),
do_univariate_greml("Male fitness early"),
do_univariate_greml("Male fitness late")) %>%
filter(Source %in% c("V(G)", "V(e)", "Vp", "V(G)/Vp")) %>%
mutate(Variance = as.numeric(Variance)) %>%
rename(Parameter = Source)
univariate_table %>%
kable(digits = 3) %>%
kable_styling(full_width = FALSE)| Trait | Parameter | Variance | SE |
|---|---|---|---|
| Female fitness early | V(G) | 0.121 | 0.221 |
| Female fitness early | V(e) | 0.765 | 0.443 |
| Female fitness early | Vp | 0.886 | 0.240 |
| Female fitness early | V(G)/Vp | 0.137 | 0.281 |
| Female fitness late | V(G) | 0.388 | 0.202 |
| Female fitness late | V(e) | 0.226 | 0.362 |
| Female fitness late | Vp | 0.614 | 0.183 |
| Female fitness late | V(G)/Vp | 0.632 | 0.489 |
| Male fitness early | V(G) | 0.192 | 0.194 |
| Male fitness early | V(e) | 0.613 | 0.381 |
| Male fitness early | Vp | 0.805 | 0.207 |
| Male fitness early | V(G)/Vp | 0.238 | 0.293 |
| Male fitness late | V(G) | 0.000 | 0.233 |
| Male fitness late | V(e) | 1.019 | 0.485 |
| Male fitness late | Vp | 1.019 | 0.268 |
| Male fitness late | V(G)/Vp | 0.000 | 0.229 |
The below uses the gcta64 command
--reml-bivar to estimate the genetic covariances
(C(G)_tr12), i.e. the co-variance explained by the GRM),
and the genetic correlation (rG), in addition to the
univariate parameters for trait 1 and trait 2 mentioned above. Note that
because the genetic variance is low for male late-life fitness, the
model cannot reliably estimate genetic covariance or correlations for
pairs of traits that involve male late-life fitness.
do_bivar_greml <- function(trait1, trait2){
# First make 'focal_data', a 2-column data frame with the line and the focal phenotype value
focal_data <- line_means %>%
select(line, !! trait1, !! trait2)
names(focal_data)[2:3] <- c("focal_pheno1", "focal_pheno2")
# Write a file with the line and phenotype data called phenotype.txt, for gcta64
pheno_data <- focal_data %>%
# filter(!(line %in% lines_to_prune)) %>%
mutate(line_copy = line) %>%
select(line, line_copy, focal_pheno1, focal_pheno2) %>%
as.matrix()
pheno_data %>%
write.table(row.names = FALSE, col.names = FALSE,
file = "data/derived/phenotype_pair.txt", quote = FALSE)
# Note that the warnings come when gcta could not fit the model properly. I save these errors especially, so no need to print them.
console_output <- suppressWarnings(capture.output(
run_command(glue("{gcta} --reml-bivar --reml-bivar-lrt-rg 0 --reml-maxit 10000 --grm gcta_GRM --pheno phenotype_pair.txt --out delete_me"),
path = "/data/derived/")))
errors <- console_output[str_detect(console_output, "[Ee]rror")]
if(length(errors) == 0) errors <- NA
if(length(errors) > 1) errors <- paste(errors, sep = "; ")
traits <- sort(c(trait1, trait2))
rbind(read.delim("data/derived/delete_me.hsq", sep = "\t") ,
data.frame(Source = "Errors", Variance = errors, SE = NA)) %>%
mutate(`Trait 1` = traits[1], `Trait 2` = traits[2]) %>% select(`Trait 1`, `Trait 2`, everything())
}
results <- bind_rows(do_bivar_greml("Female fitness early", "Female fitness late"),
do_bivar_greml("Male fitness early", "Male fitness late"),
do_bivar_greml("Female fitness early", "Male fitness early"),
do_bivar_greml("Female fitness late", "Male fitness late"),
do_bivar_greml("Female fitness early", "Male fitness late"),
do_bivar_greml("Female fitness late", "Male fitness early")) %>%
filter(Source %in% c("V(G)_tr1", "V(e)_tr1", "Vp_tr1", "V(G)/Vp_tr1",
"V(G)_tr2", "V(e)_tr2", "Vp_tr2", "V(G)/Vp_tr2",
"C(G)_tr12", "rG")) %>%
mutate(Variance = as.numeric(Variance)) %>%
rename(Parameter = Source)
unlink(c("data/derived/delete_me.log", "data/derived/phenotype_pair.txt", "data/derived/delete_me.hsq"))
unlink(list.files("data/derived/", pattern = "gcta_GRM", full.names = TRUE))
results %>%
kable(digit = 3) %>%
kable_styling(full_width = FALSE)| Trait 1 | Trait 2 | Parameter | Variance | SE |
|---|---|---|---|---|
| Female fitness early | Female fitness late | V(G)_tr1 | 0.167 | 0.208 |
| Female fitness early | Female fitness late | V(G)_tr2 | 0.433 | 0.170 |
| Female fitness early | Female fitness late | C(G)_tr12 | 0.223 | 0.173 |
| Female fitness early | Female fitness late | V(e)_tr1 | 0.668 | 0.410 |
| Female fitness early | Female fitness late | V(e)_tr2 | 0.153 | 0.292 |
| Female fitness early | Female fitness late | Vp_tr1 | 0.836 | 0.221 |
| Female fitness early | Female fitness late | Vp_tr2 | 0.586 | 0.151 |
| Female fitness early | Female fitness late | V(G)/Vp_tr1 | 0.200 | 0.294 |
| Female fitness early | Female fitness late | V(G)/Vp_tr2 | 0.739 | 0.439 |
| Female fitness early | Female fitness late | rG | 0.827 | 0.230 |
| Male fitness early | Male fitness late | V(G)_tr1 | 0.110 | 0.186 |
| Male fitness early | Male fitness late | V(G)_tr2 | 0.001 | 0.234 |
| Male fitness early | Male fitness late | C(G)_tr12 | -0.010 | 0.197 |
| Male fitness early | Male fitness late | V(e)_tr1 | 0.781 | 0.383 |
| Male fitness early | Male fitness late | V(e)_tr2 | 1.001 | 0.486 |
| Male fitness early | Male fitness late | Vp_tr1 | 0.891 | 0.216 |
| Male fitness early | Male fitness late | Vp_tr2 | 1.002 | 0.268 |
| Male fitness early | Male fitness late | V(G)/Vp_tr1 | 0.124 | 0.234 |
| Male fitness early | Male fitness late | V(G)/Vp_tr2 | 0.001 | 0.234 |
| Male fitness early | Male fitness late | rG | -1.000 | 147.954 |
| Female fitness early | Male fitness early | V(G)_tr1 | 0.123 | 0.222 |
| Female fitness early | Male fitness early | V(G)_tr2 | 0.192 | 0.195 |
| Female fitness early | Male fitness early | C(G)_tr12 | 0.047 | 0.153 |
| Female fitness early | Male fitness early | V(e)_tr1 | 0.760 | 0.445 |
| Female fitness early | Male fitness early | V(e)_tr2 | 0.612 | 0.382 |
| Female fitness early | Male fitness early | Vp_tr1 | 0.883 | 0.241 |
| Female fitness early | Male fitness early | Vp_tr2 | 0.805 | 0.208 |
| Female fitness early | Male fitness early | V(G)/Vp_tr1 | 0.139 | 0.284 |
| Female fitness early | Male fitness early | V(G)/Vp_tr2 | 0.239 | 0.294 |
| Female fitness early | Male fitness early | rG | 0.306 | 0.888 |
| Female fitness late | Male fitness late | V(G)_tr1 | 0.351 | 0.140 |
| Female fitness late | Male fitness late | V(G)_tr2 | 0.071 | 0.282 |
| Female fitness late | Male fitness late | C(G)_tr12 | 0.158 | 0.139 |
| Female fitness late | Male fitness late | V(e)_tr1 | 0.119 | 0.244 |
| Female fitness late | Male fitness late | V(e)_tr2 | 0.963 | 0.570 |
| Female fitness late | Male fitness late | Vp_tr1 | 0.470 | 0.122 |
| Female fitness late | Male fitness late | Vp_tr2 | 1.034 | 0.306 |
| Female fitness late | Male fitness late | V(G)/Vp_tr1 | 0.747 | 0.459 |
| Female fitness late | Male fitness late | V(G)/Vp_tr2 | 0.069 | 0.291 |
| Female fitness late | Male fitness late | rG | 1.000 | 2.158 |
| Female fitness early | Male fitness late | V(G)_tr1 | 0.124 | 0.218 |
| Female fitness early | Male fitness late | V(G)_tr2 | 0.001 | 0.233 |
| Female fitness early | Male fitness late | C(G)_tr12 | -0.012 | 0.159 |
| Female fitness early | Male fitness late | V(e)_tr1 | 0.756 | 0.437 |
| Female fitness early | Male fitness late | V(e)_tr2 | 1.016 | 0.484 |
| Female fitness early | Male fitness late | Vp_tr1 | 0.880 | 0.237 |
| Female fitness early | Male fitness late | Vp_tr2 | 1.017 | 0.267 |
| Female fitness early | Male fitness late | V(G)/Vp_tr1 | 0.141 | 0.281 |
| Female fitness early | Male fitness late | V(G)/Vp_tr2 | 0.001 | 0.229 |
| Female fitness early | Male fitness late | rG | -1.000 | 95.966 |
| Female fitness late | Male fitness early | V(G)_tr1 | 0.495 | 0.186 |
| Female fitness late | Male fitness early | V(G)_tr2 | 0.241 | 0.164 |
| Female fitness late | Male fitness early | C(G)_tr12 | 0.266 | 0.122 |
| Female fitness late | Male fitness early | V(e)_tr1 | 0.062 | 0.311 |
| Female fitness late | Male fitness early | V(e)_tr2 | 0.490 | 0.311 |
| Female fitness late | Male fitness early | Vp_tr1 | 0.557 | 0.154 |
| Female fitness late | Male fitness early | Vp_tr2 | 0.731 | 0.170 |
| Female fitness late | Male fitness early | V(G)/Vp_tr1 | 0.889 | 0.531 |
| Female fitness late | Male fitness early | V(G)/Vp_tr2 | 0.329 | 0.285 |
| Female fitness late | Male fitness early | rG | 0.770 | 0.353 |
G_mat <- bind_rows(
univariate_table %>% filter(Parameter == "V(G)") %>% mutate(`Trait 1` = Trait, `Trait 2` = Trait) %>% select(`Trait 1`, `Trait 2`, vcov = Variance, SE),
results %>% filter(Parameter == "C(G)_tr12") %>% select(`Trait 1`, `Trait 2`, vcov = Variance, SE)) %>%
mutate(text = paste(format(round(vcov, 2), nsmall = 2), " (", format(round(SE, 2), nsmall = 2), ")", sep = "")) %>%
select(-vcov, -SE) %>%
spread(`Trait 1`, text)
rownames(G_mat) <- G_mat[,1]
G_mat <- G_mat[,-1]
G_mat[is.na(G_mat)] <- ""
saveRDS(G_mat, "data/derived/G_mat.rds")
G_mat %>% kable() %>% kable_styling()| Female fitness early | Female fitness late | Male fitness early | Male fitness late | |
|---|---|---|---|---|
| Female fitness early | 0.12 (0.22) | |||
| Female fitness late | 0.22 (0.17) | 0.39 (0.20) | ||
| Male fitness early | 0.05 (0.15) | 0.27 (0.12) | 0.19 (0.19) | |
| Male fitness late | -0.01 (0.16) | 0.16 (0.14) | -0.01 (0.20) | 0.00 (0.23) |
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] brms_2.16.3 Rcpp_1.0.4.6 kableExtra_1.3.4 glue_1.4.2
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_2.1.2 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.6
[13] tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.7 systemfonts_0.2.2
[4] plyr_1.8.6 igraph_1.2.5 splines_4.0.3
[7] crosstalk_1.1.0.1 TH.data_1.0-10 rstantools_2.1.1
[10] inline_0.3.15 digest_0.6.29 htmltools_0.5.2
[13] rsconnect_0.8.16 fansi_0.4.1 magrittr_2.0.1
[16] checkmate_2.0.0 googlesheets4_1.0.0 tzdb_0.1.2
[19] modelr_0.1.8 RcppParallel_5.0.1 matrixStats_0.56.0
[22] vroom_1.5.7 xts_0.12-0 sandwich_2.5-1
[25] svglite_1.2.3 prettyunits_1.1.1 colorspace_2.0-3
[28] rvest_1.0.2 haven_2.5.0 xfun_0.31
[31] callr_3.7.0 crayon_1.4.2 jsonlite_1.8.0
[34] lme4_1.1-23 survival_3.2-7 zoo_1.8-10
[37] gtable_0.3.0 gargle_1.2.0 emmeans_1.4.7
[40] webshot_0.5.2 V8_3.4.0 distributional_0.3.0
[43] pkgbuild_1.0.8 rstan_2.21.2 abind_1.4-5
[46] scales_1.2.0 mvtnorm_1.1-0 DBI_1.1.0
[49] miniUI_0.1.1.1 viridisLite_0.3.0 xtable_1.8-4
[52] bit_1.1-15.2 stats4_4.0.3 StanHeaders_2.21.0-3
[55] DT_0.13 htmlwidgets_1.5.4 httr_1.4.2
[58] threejs_0.3.3 posterior_1.2.0 ellipsis_0.3.2
[61] pkgconfig_2.0.3 loo_2.3.1 farver_2.0.3
[64] dbplyr_2.1.1 utf8_1.1.4 tidyselect_1.1.0
[67] rlang_1.0.2 reshape2_1.4.4 later_1.0.0
[70] munsell_0.5.0 cellranger_1.1.0 tools_4.0.3
[73] cli_3.1.0 generics_0.0.2 broom_0.7.11
[76] ggridges_0.5.2 evaluate_0.15 fastmap_1.1.0
[79] yaml_2.3.5 bit64_0.9-7 processx_3.5.2
[82] knitr_1.39 fs_1.4.1 nlme_3.1-149
[85] projpred_2.0.2 whisker_0.4 mime_0.9
[88] xml2_1.3.3 compiler_4.0.3 bayesplot_1.7.2
[91] shinythemes_1.1.2 rstudioapi_0.13 gamm4_0.2-6
[94] curl_4.3 reprex_2.0.1 statmod_1.4.34
[97] stringi_1.5.3 highr_0.8 ps_1.3.3
[100] Brobdingnag_1.2-6 gdtools_0.2.2 lattice_0.20-41
[103] Matrix_1.2-18 nloptr_1.2.2.1 markdown_1.1
[106] shinyjs_1.1 tensorA_0.36.1 vctrs_0.3.8
[109] pillar_1.6.4.9003 lifecycle_1.0.1 jquerylib_0.1.1
[112] bridgesampling_1.0-0 estimability_1.3 httpuv_1.5.3.1
[115] R6_2.4.1 promises_1.1.0 gridExtra_2.3
[118] codetools_0.2-16 boot_1.3-25 MASS_7.3-53
[121] colourpicker_1.0 gtools_3.8.2 assertthat_0.2.1
[124] rprojroot_1.3-2 withr_2.4.3 shinystan_2.5.0
[127] multcomp_1.4-13 mgcv_1.8-33 parallel_4.0.3
[130] hms_1.1.1 grid_4.0.3 minqa_1.2.4
[133] coda_0.19-3 rmarkdown_2.11 googledrive_2.0.0
[136] git2r_0.27.1 getPass_0.2-2 shiny_1.4.0.2
[139] lubridate_1.8.0 base64enc_0.1-3 dygraphs_1.1.1.6