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| File | Version | Author | Date | Message |
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| Rmd | edeae3c | Eric Tytell | 2021-12-30 | Rename notebooks to indicate order |
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.4 ✓ dplyr 1.0.7
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 2.0.1 ✓ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(ggbeeswarm)
library(phytools)Loading required package: apeLoading required package: maps
Attaching package: 'maps'The following object is masked from 'package:purrr':
maplibrary(patchwork)
library(here)here() starts at /Users/etytel01/Documents/Vertebrae/Codelibrary(nlme)
Attaching package: 'nlme'The following object is masked from 'package:dplyr':
collapselibrary(ape)
library(geiger)
library(ggtree)ggtree v3.0.2 For help: https://yulab-smu.top/treedata-book/
If you use ggtree in published research, please cite the most appropriate paper(s):
1. Guangchuang Yu. Using ggtree to visualize data on tree-like structures. Current Protocols in Bioinformatics, 2020, 69:e96. doi:10.1002/cpbi.96
2. Guangchuang Yu, Tommy Tsan-Yuk Lam, Huachen Zhu, Yi Guan. Two methods for mapping and visualizing associated data on phylogeny using ggtree. Molecular Biology and Evolution 2018, 35(12):3041-3043. doi:10.1093/molbev/msy194
3. Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution 2017, 8(1):28-36. doi:10.1111/2041-210X.12628
Attaching package: 'ggtree'The following object is masked from 'package:nlme':
collapseThe following object is masked from 'package:ape':
rotateThe following object is masked from 'package:tidyr':
expandlibrary(emmeans)
library(car)Loading required package: carData
Attaching package: 'car'The following object is masked from 'package:dplyr':
recodeThe following object is masked from 'package:purrr':
somelibrary(Hmisc)Loading required package: latticeLoading required package: survivalLoading required package: Formula
Attaching package: 'Hmisc'The following object is masked from 'package:ape':
zoomThe following objects are masked from 'package:dplyr':
src, summarizeThe following objects are masked from 'package:base':
format.pval, unitscitation()
To cite R in publications use:
R Core Team (2021). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
URL https://www.R-project.org/.
A BibTeX entry for LaTeX users is
@Manual{,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2021},
url = {https://www.R-project.org/},
}
We have invested a lot of time and effort in creating R, please cite it
when using it for data analysis. See also 'citation("pkgname")' for
citing R packages.print(getRversion())[1] '4.1.2'citation("nlme")
Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). _nlme:
Linear and Nonlinear Mixed Effects Models_. R package version 3.1-153,
<URL: https://CRAN.R-project.org/package=nlme>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {{nlme}: Linear and Nonlinear Mixed Effects Models},
author = {Jose Pinheiro and Douglas Bates and Saikat DebRoy and Deepayan Sarkar and {R Core Team}},
year = {2021},
note = {R package version 3.1-153},
url = {https://CRAN.R-project.org/package=nlme},
}packageVersion("nlme")[1] '3.1.153'citation("ape")
To cite ape in a publication please use:
Paradis E. & Schliep K. 2019. ape 5.0: an environment for modern
phylogenetics and evolutionary analyses in R. Bioinformatics 35:
526-528.
A BibTeX entry for LaTeX users is
@Article{,
title = {ape 5.0: an environment for modern phylogenetics and evolutionary analyses in {R}},
author = {E. Paradis and K. Schliep},
journal = {Bioinformatics},
year = {2019},
volume = {35},
pages = {526-528},
}
As ape is evolving quickly, you may want to cite also its version
number (found with 'library(help = ape)' or 'packageVersion("ape")').packageVersion("ape")[1] '5.5'citation("geiger")
To cite medusa, auteur, or geiger in a publication use:
medusa
Alfaro Michael E, Francesco Santini, Chad Brock, Hugo Alamillo, Alex
Dornburg, Daniel L Rabosky, Giorgio Carnevale, and Luke J Harmon.
2009. Nine exceptional radiations plus high turnover explain species
diversity in jawed vertebrates. PNAS 106:13410-13414.
auteur
Eastman Jonathan M, Michael E Alfaro, Paul Joyce, Andrew L Hipp, and
Luke J Harmon. 2011. A novel comparative method for identifying
shifts in the rate of character evolution on trees. Evolution
65:3578-3589.
MECCA
Slater Graham J, Luke J Harmon, Daniel Wegmann, Paul Joyce, Liam J
Revell, and Michael E Alfaro. 2012. Fitting models of continuous
trait evolution to incompletely sampled comparative data using
approximate Bayesian computation. Evolution 66:752-762.
geiger-orig
Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and
Wendell Challenger. 2008. GEIGER: investigating evolutionary
radiations. Bioinformatics 24:129-131.
geiger
Pennell Matthew W, Jonathan M Eastman, Graham J Slater, Joseph W
Brown, Josef C Uyeda, Richard G FitzJohn, Michael E Alfaro, and Luke
J Harmon. 2014. geiger v2.0: an expanded suite of methods for fitting
macroevolutionary models to phylogenetic trees. Bioinformatics
30:2216-2218.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.packageVersion("geiger")[1] '2.0.7'Load data
vertdata_sum <- read_csv(here("output/vertdata_summary_lm.csv"))Rows: 83 Columns: 90── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): Species, Habitat, Water_Type, MatchSpecies, MatchGenus
dbl (85): fineness, CBL_med, CBL_max, CBL_mn, d_med, d_max, d_mn, alphaAnt_m...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Phylogeny
This is the whole Betancur-R tree.
tree <- read.tree(here('data/12862_2017_958_MOESM2_ESM.tre'))Get the names of species from the tree.
allspecies <- tibble(tree$tip.label)
colnames(allspecies) <- c('FullName')
head(allspecies)# A tibble: 6 × 1
FullName
<chr>
1 Rajidae_Leucoraja_erinacea_G01356
2 Callorhinchidae_Callorhinchus_milii_G01235
3 Latimeriidae_Latimeria_chalumnae_G01347
4 Neoceratodontidae_Neoceratodus_forsteri_G01534
5 Protopteridae_Protopterus_aethiopicus_annectens_G01451
6 Lepidosirenidae_Lepidosiren_paradoxa_G01352 And split the names into family, genus, and species.
allspecies <-
allspecies %>% separate(FullName, sep='_', into=c('Family', 'Genus', 'Species'),
extra='drop', remove=FALSE)Set up the tip number (just the row)
allspecies$Tip <- seq_len(nrow(allspecies))vertdata <- left_join(vertdata_sum, allspecies,
by=c("MatchGenus"="Genus", "MatchSpecies"="Species")) %>%
select(-(ends_with(".x") | ends_with(".y")))
vertdata# A tibble: 83 × 93
Species Habitat Water_Type MatchSpecies MatchGenus fineness CBL_med CBL_max
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Abramis_… pelagic freshwater alburnus Alburnus 8.95 0.0166 0.0177
2 Alectis_… demers… marine ciliaris Alectis 8.75 0.0346 0.0357
3 Alosa_ps… pelagic anadromous pseudoharen… Alosa 7.39 0.0165 0.0173
4 Amia_cal… demers… freshwater calva Amia 6.72 0.00983 0.0113
5 Ammodyte… benthic marine dubius Ammodytes 16.9 0.0132 0.0141
6 Anodonto… pelagic freshwater cepedianum Dorosoma 4.66 0.0228 0.0238
7 Anoplarc… benthic marine <NA> <NA> 8.62 0.0195 0.0212
8 Anoplarc… benthic marine <NA> <NA> 9.51 0.0156 0.0176
9 Anoploga… pelagic marine cornuta Anoplogas… 5.04 0.0287 0.0329
10 Aphareus… pelagic marine furca Aphareus 5.01 0.0312 0.0334
# … with 73 more rows, and 85 more variables: CBL_mn <dbl>, d_med <dbl>,
# d_max <dbl>, d_mn <dbl>, alphaAnt_med <dbl>, alphaAnt_max <dbl>,
# alphaAnt_mn <dbl>, alphaPos_med <dbl>, alphaPos_max <dbl>,
# alphaPos_mn <dbl>, DAnt_med <dbl>, DAnt_max <dbl>, DAnt_mn <dbl>,
# DPos_med <dbl>, DPos_max <dbl>, DPos_mn <dbl>, dBW_med <dbl>,
# dBW_max <dbl>, dBW_mn <dbl>, DAntBW_med <dbl>, DAntBW_max <dbl>,
# DAntBW_mn <dbl>, DPosBW_med <dbl>, DPosBW_max <dbl>, DPosBW_mn <dbl>, …Drop species without a match
vertdata %>%
filter(is.na(Tip)) %>%
distinct(Species, .keep_all=TRUE) %>%
select(Species, MatchGenus, MatchSpecies, Tip, Habitat)# A tibble: 4 × 5
Species MatchGenus MatchSpecies Tip Habitat
<chr> <chr> <chr> <int> <chr>
1 Anoplarchus_insignis <NA> <NA> NA benthic
2 Anoplarchus_purpurescens <NA> <NA> NA benthic
3 Xiphister_atropurpureus <NA> <NA> NA benthic
4 Xiphister_mucosus <NA> <NA> NA benthicvertdata <-
vertdata %>%
filter(!is.na(Tip))ourspecies <-
vertdata %>%
distinct(Species, .keep_all=TRUE)verttree <- keep.tip(tree, tip=as.vector(ourspecies$Tip))plotTree(verttree)
vertdata_sp <-
vertdata %>%
distinct(FullName, .keep_all = TRUE) %>%
mutate(rowname = FullName) %>%
column_to_rownames(var = "rowname")verttree <- keep.tip(tree, tip=as.vector(vertdata_sp$Tip))left_join(as_tibble(verttree),
vertdata_sp %>%
rownames_to_column("label") %>%
select(label, Habitat)) %>%
tidytree::as.treedata() %>%
ggtree(layout = 'circular') + # geom_tiplab() +
geom_tippoint(aes(color = Habitat))Joining, by = "label"
Check if tree and data match
length(verttree$tip.label)[1] 77nrow(vertdata_sp)[1] 77name.check(verttree, vertdata_sp)[1] "OK"Merge the measurements and the tree
verttree_data <-
as_tibble(verttree) %>%
left_join(vertdata_sp %>%
rownames_to_column("label") %>%
select(label, Habitat, Family,
ends_with("80"), ends_with("max"), ends_with("med"), ends_with("slope"), ends_with("quad")))Joining, by = "label"vertdata_sp <-
vertdata_sp %>%
mutate(CBL_vtx = -CBL_slope / CBL_quad,
alphaPos_vtx = -alphaPos_slope / alphaPos_quad,
alphaAnt_vtx = -alphaAnt_slope / alphaAnt_quad,
d_vtx = -d_slope / d_quad,
DAnt_vtx = -DAnt_slope / DAnt_quad,
DPos_vtx = -DPos_slope / DPos_quad)Save out the tree data. There is something subtly different about saving the data out as a csv file and saving it as an RDS file. The base class of the tree is tbl_tree, but when we load it back in from a csv, despite having exactly the same data, it won’t work with the tidytree functions. So we save it in an RDS file, which preserves the class.
write_csv(vertdata_sp, here('output/vertdata_summary_lm_species.csv'))
saveRDS(verttree, here('output/vert_tree.rds'))PGLS Analysis for means, maxes, medians, and 80%
vertdata_sp0 <-
vertdata_sp %>%
mutate(across(contains('slope') | contains('quad'),
~ replace_na(., 0)))var <- c(#'CBL_80', 'd_80', 'alphaAnt_80', 'alphaPos_80', 'DAnt_80', 'DPos_80',
#'CBL_max', 'd_max', 'alphaAnt_max', 'alphaPos_max', 'DAnt_max', 'DPos_max',
'CBL_mn', 'd_mn', 'alphaAnt_mn', 'alphaPos_mn', 'DAnt_mn', 'DPos_mn',
#'CBL_med', 'd_med', 'alphaAnt_med', 'alphaPos_med', 'DAnt_med', 'DPos_med',
'fineness',
#'CBL_slope', 'd_slope', 'alphaAnt_slope', 'alphaPos_slope', 'DAnt_slope', 'DPos_slope',
'CBL_vtx', 'd_vtx', 'alphaAnt_vtx', 'alphaPos_vtx', 'DAnt_vtx', 'DPos_vtx',
'CBL_quad', 'd_quad', 'alphaAnt_quad', 'alphaPos_quad', 'DAnt_quad', 'DPos_quad')
#'CBL_order', 'd_order', 'alphaAnt_order', 'alphaPos_order', 'DAnt_order', 'DPos_order')
modeltests <- tibble()
for (i in seq_along(var)) {
print(var[i])
fmla <- as.formula(paste(var[i], "Habitat", sep = " ~ "))
mod <- gls(fmla, correlation = corBrownian(1, phy = verttree, form = ~FullName),
data = vertdata_sp0, method = "ML")
ava <- broom::tidy(Anova(mod))
ava$var = var[i]
ava$model = list(mod)
modeltests <- bind_rows(modeltests, ava)
}[1] "CBL_mn"
[1] "d_mn"
[1] "alphaAnt_mn"
[1] "alphaPos_mn"
[1] "DAnt_mn"
[1] "DPos_mn"
[1] "fineness"
[1] "CBL_vtx"
[1] "d_vtx"
[1] "alphaAnt_vtx"
[1] "alphaPos_vtx"
[1] "DAnt_vtx"
[1] "DPos_vtx"
[1] "CBL_quad"
[1] "d_quad"
[1] "alphaAnt_quad"
[1] "alphaPos_quad"
[1] "DAnt_quad"
[1] "DPos_quad"modeltests %>%
dplyr::select(var, statistic, p.value, everything()) %>%
filter(p.value < 0.05 | str_detect(var, "dBW")) %>%
arrange(var)# A tibble: 5 × 6
var statistic p.value term df model
<chr> <dbl> <dbl> <chr> <dbl> <list>
1 alphaPos_mn 20.1 0.0000437 Habitat 2 <gls>
2 CBL_quad 11.1 0.00385 Habitat 2 <gls>
3 CBL_vtx 6.42 0.0404 Habitat 2 <gls>
4 d_mn 8.61 0.0135 Habitat 2 <gls>
5 d_vtx 21.2 0.0000251 Habitat 2 <gls> compare_habitats <- function(model) {
emm <- emmeans(model, ~Habitat)
p <- as.data.frame(pairs(emm))
es <- as.data.frame(eff_size(emm, sigma = sigma(model), edf = model$dims$N - model$dims$p))
p$effect.size <- es$effect.size
p %>%
mutate(contrast = str_replace(contrast, "(\\w+) - (\\w+)", "\\1_\\2")) %>%
select(contrast, p.value, effect.size) %>%
rename(p = p.value, eff = effect.size) %>%
pivot_wider(names_from = contrast, values_from = c(p, eff),
names_glue = "{contrast}_{.value}")
}habitat_means <- function(model) {
emm <- emmeans(model, ~Habitat)
as.data.frame(emm) %>%
select(Habitat, emmean, SE) %>%
rename(mn = emmean,
se = SE) %>%
pivot_wider(names_from = Habitat, values_from = c(mn, se),
names_glue = "{Habitat}_{.value}")
}modeltests <-
modeltests %>%
dplyr::select(var, statistic, p.value, everything()) %>%
# filter(p.value < 0.05) %>%
mutate(mc = purrr::map(model, compare_habitats)) %>%
unnest(mc) %>%
mutate(total_eff = abs(benthic_demersal_eff) + abs(benthic_pelagic_eff) + abs(demersal_pelagic_eff)) %>%
mutate(hm = purrr::map(model, habitat_means)) %>%
unnest(hm) %>%
# filter(total_eff > 0.5) %>%
relocate(p.value, total_eff, ends_with("_p"), .after = var) %>%
arrange(desc(total_eff))modeltests %>%
filter(abs(benthic_demersal_eff) >= 0.2 | abs(benthic_pelagic_eff) >= 0.2 | abs(demersal_pelagic_eff) >= 0.2)# A tibble: 12 × 19
var p.value total_eff benthic_demersa… benthic_pelagic… demersal_pelagi…
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 alphaPos_mn 0.0000437 1.05 0.000899 0.00191 0.413
2 CBL_quad 0.00385 0.963 0.635 0.0231 0.00378
3 d_mn 0.0135 0.860 0.805 0.0132 0.0310
4 d_vtx 0.0000251 0.799 0.0000937 0.0239 1.00
5 CBL_vtx 0.0404 0.730 0.670 0.133 0.0359
6 alphaAnt_mn 0.0948 0.626 0.884 0.168 0.0836
7 DAnt_quad 0.208 0.524 0.855 0.186 0.306
8 alphaAnt_quad 0.167 0.521 0.556 0.499 0.175
9 DPos_quad 0.271 0.472 0.947 0.253 0.326
10 fineness 0.279 0.456 0.788 0.492 0.260
11 alphaPos_quad 0.216 0.448 0.452 0.718 0.274
12 DPos_mn 0.340 0.410 0.735 0.623 0.336
# … with 13 more variables: statistic <dbl>, term <chr>, df <dbl>,
# model <list>, benthic_demersal_eff <dbl>, benthic_pelagic_eff <dbl>,
# demersal_pelagic_eff <dbl>, benthic_mn <dbl>, demersal_mn <dbl>,
# pelagic_mn <dbl>, benthic_se <dbl>, demersal_se <dbl>, pelagic_se <dbl>emmeans(modeltests$model[[2]], ~Habitat) Habitat emmean SE df lower.CL upper.CL
benthic -0.02279 0.0386 74 -0.0997 0.0541
demersal -0.02882 0.0381 74 -0.1048 0.0472
pelagic 0.00649 0.0392 74 -0.0716 0.0846
Degrees-of-freedom method: df.error
Confidence level used: 0.95 sigvars <- pull(modeltests, var)write_csv(modeltests, here("output/modeltests.csv"))
saveRDS(modeltests, here("output/PGLSmodels.Rds"))Mean plots with stats
This sets up the data in long form, with all the variables stacked, and the variables ordered by descending total effect size.
vertdata_long <-
vertdata_sp0 %>%
select(Habitat, Species, !!sigvars) %>%
pivot_longer(cols = !Habitat & !Species, names_to = "var", values_to = "value") %>%
left_join(
modeltests %>%
select(var, total_eff, p.value),
by = "var") %>%
mutate(var = as.factor(var),
var = fct_reorder(var, total_eff, .desc = TRUE))Plot all the effects
vertdata_long %>%
filter(!is.na(var) & p.value < 0.05) %>%
ggplot(aes(x = Habitat, y = value, color = Habitat)) +
geom_quasirandom(width=0.3, alpha = 0.5) +
stat_summary(fun.data = mean_sdl) +
# geom_boxplot(width=0.3, alpha=0.5, outlier.shape = NA) +
stat_summary(aes(group = 1), fun = "mean", geom = "line") +
facet_wrap(~ var, scales = "free_y")
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] Hmisc_4.5-0 Formula_1.2-4 survival_3.2-13 lattice_0.20-45
[5] car_3.0-11 carData_3.0-4 emmeans_1.6.3 ggtree_3.0.2
[9] geiger_2.0.7 nlme_3.1-153 here_1.0.1 patchwork_1.1.1
[13] phytools_0.7-80 maps_3.3.0 ape_5.5 ggbeeswarm_0.6.0
[17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[21] readr_2.0.1 tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5
[25] tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 fastmatch_1.1-3
[4] workflowr_1.7.0 plyr_1.8.6 igraph_1.2.6
[7] lazyeval_0.2.2 splines_4.1.2 digest_0.6.27
[10] yulab.utils_0.0.2 htmltools_0.5.2 fansi_0.5.0
[13] checkmate_2.0.0 magrittr_2.0.1 cluster_2.1.2
[16] tzdb_0.1.2 openxlsx_4.2.4 modelr_0.1.8
[19] vroom_1.5.4 jpeg_0.1-9 colorspace_2.0-2
[22] rvest_1.0.1 haven_2.4.3 xfun_0.25
[25] crayon_1.4.1 jsonlite_1.7.2 phangorn_2.7.1
[28] glue_1.4.2 gtable_0.3.0 abind_1.4-5
[31] scales_1.1.1 mvtnorm_1.1-2 DBI_1.1.1
[34] Rcpp_1.0.7 plotrix_3.8-2 htmlTable_2.2.1
[37] xtable_1.8-4 tmvnsim_1.0-2 gridGraphics_0.5-1
[40] tidytree_0.3.5 bit_4.0.4 foreign_0.8-81
[43] subplex_1.6 deSolve_1.28 htmlwidgets_1.5.4
[46] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
[49] farver_2.1.0 pkgconfig_2.0.3 nnet_7.3-16
[52] dbplyr_2.1.1 utf8_1.2.2 labeling_0.4.2
[55] ggplotify_0.1.0 tidyselect_1.1.1 rlang_0.4.11
[58] later_1.3.0 munsell_0.5.0 cellranger_1.1.0
[61] tools_4.1.2 cli_3.0.1 generics_0.1.0
[64] broom_0.7.9 evaluate_0.14 fastmap_1.1.0
[67] yaml_2.2.1 bit64_4.0.5 knitr_1.34
[70] fs_1.5.0 zip_2.2.0 whisker_0.4
[73] aplot_0.1.0 xml2_1.3.2 compiler_4.1.2
[76] rstudioapi_0.13 beeswarm_0.4.0 curl_4.3.2
[79] png_0.1-7 reprex_2.0.1 treeio_1.16.1
[82] clusterGeneration_1.3.7 stringi_1.7.4 highr_0.9
[85] Matrix_1.3-4 vctrs_0.3.8 pillar_1.6.2
[88] lifecycle_1.0.0 BiocManager_1.30.16 combinat_0.0-8
[91] estimability_1.3 data.table_1.14.0 httpuv_1.6.4
[94] R6_2.5.1 latticeExtra_0.6-29 promises_1.2.0.1
[97] renv_0.14.0 gridExtra_2.3 rio_0.5.27
[100] vipor_0.4.5 codetools_0.2-18 MASS_7.3-54
[103] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2
[106] mnormt_2.0.2 expm_0.999-6 parallel_4.1.2
[109] hms_1.1.0 quadprog_1.5-8 grid_4.1.2
[112] rpart_4.1-15 coda_0.19-4 rmarkdown_2.10
[115] rvcheck_0.1.8 git2r_0.29.0 numDeriv_2016.8-1.1
[118] scatterplot3d_0.3-41 lubridate_1.7.10 base64enc_0.1-3