# 2) Example of GMA-SMA models for varietal mixtures in microplots

#### T. Flutre

#### 21/06/2026

Abstract

This document aims at simulating data with GMA-SMA models, fitting them with various softwares, and checking their estimates.

# 1 Preamble

Dependencies:

    suppressPackageStartupMessages(library(plantmix))
    suppressPackageStartupMessages(library(lme4))

# 2 Overview

In this vignette, varietal mixtures will be assembled from a panel of 200 genotypes. Only 1000 mixtures will be observed, among all 1.99^{4} possible ones, i.e., approximately 5%. This incomplete design will be optimized so that it is balanced: all genotype will be observed in the same number of mixtures. Phenotypes will then be simulated organized into a field trial in a randomized complete block design, according to all six GMA-SMA models that can be conceived: 1, 2, 2’, 2’‘, 3 and 3’. See the article by Forst et al (2019), especially for the first three models, as well as the introductory vignette.

# 3 Simulate data

    set.seed(12345)

## 3.1 Simulate the genotypes

### 3.1.1 Set the dimensions

    nbGenos <- 200
    levGenos <- sprintf(
      fmt = paste0("g%0", floor(log10(nbGenos)) + 1, "i"),
      1:nbGenos
    )

    nbSnps <- 1000
    levSnps <- sprintf(
      fmt = paste0("s%0", floor(log10(nbSnps)) + 1, "i"),
      1:nbSnps
    )

### 3.1.2 SNP genotypes

    nb_pops <- 10
    weak_div_pops <- diag(nb_pops)
    weak_div_pops[upper.tri(weak_div_pops)] <- 0.9
    weak_div_pops[lower.tri(weak_div_pops)] <- weak_div_pops[upper.tri(weak_div_pops)]
    tmp <- rep(nbGenos / nb_pops, nb_pops - 1)
    tmp <- c(tmp, nbGenos - sum(tmp))
    snpGenos <- simulGenosDoseStruct(
      nb_genos = tmp,
      nb_snps = nbSnps,
      div_pops = weak_div_pops,
      geno_IDs = levGenos,
      snp_IDs = levSnps
    )
    dim(snpGenos)
    ## [1]  200 1000
    snpGenos[1:3, 1:4]
    ##      s0001 s0002 s0003 s0004
    ## g001     1     1     1     1
    ## g002     1     0     1     1
    ## g003     0     1     2     0

### 3.1.3 Additive genetic relationships

For simplicity, the first estimator of VanRaden (2008) is used, that assumes linkage equilibrium and Hardy-Weinberg equilibrium.

    GRM <- estimGRM(snpGenos)
    GRM <- as.matrix(Matrix::nearPD(GRM)$mat)

    image(Matrix(GRM), main = "GRM")

    hist(diag(GRM), main = "GRM")

    hist(GRM[upper.tri(GRM)], main = "GRM")

## 3.2 Simulate the phenotypes

### 3.2.1 Incomplete, yet balanced design

    nbMixes <- 1000
    design <- getDesignBinaryVarMix(levGenos, nbMixes = nbMixes)
    tmp <- getMixturesPerGeno(getMixtureList(design$combs))
    table(sapply(tmp, length)) # each genotype is in the same nb of mixtures -> balanced design
    ##
    ##  10
    ## 200

### 3.2.2 Field trial

    monoStands <- paste(levGenos, levGenos, sep = "_")

    pairs <- design$combs
    mixStands <- paste(pairs[, 1], pairs[, 2], sep = "_")
    IDs <- c(monoStands, mixStands)

    nbBlocks <- 3
    blocks <- LETTERS[1:nbBlocks]

    dat <- do.call(rbind, lapply(blocks, function(block) {
      data.frame(
        ID = IDs,
        block = block,
        stringsAsFactors = TRUE
      )
    }))

### 3.2.3 Design matrices

    listContr <- list(block = "contr.sum")
    X <- model.matrix(~ 1 + block, data = dat, contrasts = listContr)

    allZ <- list()
    allZ$GMA <- mkZGMA(dat, col = "ID", sep = "_")
    system.time(
      allZ <- append(
        allZ,
        mkAllZSMA(dat, col = "ID", sep = "_", verbose = 1)
      )
    )
    ## [1] "model 2'"
    ## [1] "model 2"
    ## [1] "model 2''"
    ## [1] "model 3"
    ## [1] "model 3'"
    ##    user  system elapsed
    ##  25.770   1.420  27.192
    sapply(allZ, dim)
    ##       GMA SMA_mod2p SMA_mod2 SMA_mod2pp_ij SMA_mod2pp_ii SMA_mod3 SMA_mod3p_ij
    ## [1,] 3600      3600     3600          3600          3600     3600         3600
    ## [2,]  200      1000     1200          1000           200     1200         1000
    ##      SMA_mod3p_ii
    ## [1,]         3600
    ## [2,]          200

### 3.2.4 Parameters

Several packages will be tested below, most notably `lme4` and `TMB`. However, `lme4` does not natively take a GRM as input. Hence the GRM will be replaced by the identity matrix to ensure a fair comparison between packages.

    truth <- list(
      "intercept" = 100,
      "var_GMA" = 10,
      "var_SMA" = 2,
      "var_SMA_ij" = 1.5,
      "var_SMA_ii" = 0.8,
      "var_error" = 1
    )
    set.seed(1234)
    truth[["blockEffs"]] <- sample(x = c(-1, 1), size = nbBlocks - 1, replace = TRUE) *
      rnorm(n = nbBlocks - 1, mean = 3, sd = 2)
    GRM <- diag(nbGenos)
    truth[["GMAs"]] <- MASS::mvrnorm(
      n = 1, mu = rep(0, nbGenos),
      Sigma = truth$var_GMA * GRM
    )
    truth[["SMAs"]] <- rnorm(n = length(IDs), mean = 0, sd = sqrt(truth$var_SMA))
    truth[["SMAs_ij"]] <- rnorm(n = length(mixStands), mean = 0, sd = sqrt(truth$var_SMA_ij))
    truth[["SMAs_ii"]] <- rnorm(n = length(monoStands), mean = 0, sd = sqrt(truth$var_SMA_ii))
    truth[["errors"]] <- rnorm(n = nrow(dat), mean = 0, sd = sqrt(truth$var_error))

### 3.2.5 Phenotypes

    y1 <- X %*% c(truth$intercept, truth$blockEffs) +
      allZ$GMA %*% truth$GMAs +
      truth$errors
    dat$pheno1 <- y1[, 1]

    y2 <- X %*% c(truth$intercept, truth$blockEffs) +
      allZ$GMA %*% truth$GMAs +
      allZ$SMA_mod2 %*% truth$SMAs +
      truth$errors
    dat$pheno2 <- y2[, 1]

    y3 <- X %*% c(truth$intercept, truth$blockEffs) +
      allZ$GMA %*% truth$GMAs +
      allZ$SMA_mod3 %*% truth$SMAs +
      truth$errors
    dat$pheno3 <- y3[, 1]

    y2p <- X %*% c(truth$intercept, truth$blockEffs) +
      allZ$GMA %*% truth$GMAs +
      allZ$SMA_mod2p %*% truth$SMAs_ij +
      truth$errors
    dat$pheno2p <- y2p[, 1]

    y2pp <- X %*% c(truth$intercept, truth$blockEffs) +
      allZ$GMA %*% truth$GMAs +
      allZ$SMA_mod2pp_ij %*% truth$SMAs_ij +
      allZ$SMA_mod2pp_ii %*% truth$SMAs_ii +
      truth$errors
    dat$pheno2pp <- y2pp[, 1]

    y3p <- X %*% c(truth$intercept, truth$blockEffs) +
      allZ$GMA %*% truth$GMAs +
      allZ$SMA_mod3p_ij %*% truth$SMAs_ij +
      allZ$SMA_mod3p_ii %*% truth$SMAs_ii +
      truth$errors
    dat$pheno3p <- y3p[, 1]

# 4 Infer the parameters

Function `fitGMASMA` allows to fit the models with various packages. In this vignette, only `lme4` and `TMB` will be used and, as you can see below, both return the same estimates. (To save time, MM4LMM is commented.)

    pkgs <- c("lme4", "TMB") # , "MM4LMM")
    if ("MM4LMM" %in% pkgs) {
      suppressPackageStartupMessages(library(MM4LMM))
    }

    runAllPkgs <- function(pkgs, form, dat, listZ, listVCov, listContr, ...) {
      ## mcMap(function(pkg) {
      Map(function(pkg) {
        print(paste0("fit with ", pkg, "..."))
        fitGMASMA(form, dat, listZ, pkg, listVCov, listContr)
        st <- system.time(
          try(fit <- fitGMASMA(form, dat, listZ, pkg, listVCov, listContr, REML = TRUE, ...))
        )
        print(st)
        fit
      }, pkgs) # , mc.cores = nbCores)
    }

## 4.1 Model 1

### 4.1.1 Fit

    form <- pheno1 ~ 1 + block
    listZ <- list("GMA" = allZ$GMA)
    listVCov <- list("GMA" = GRM)
    listContr <- list(block = "contr.sum")
    fits1 <- runAllPkgs(pkgs, form, dat, listZ, listVCov, listContr)
    ## [1] "fit with lme4..."
    ##    user  system elapsed
    ##   0.459   0.000   0.459
    ## [1] "fit with TMB..."
    ##    user  system elapsed
    ##   3.246   0.000   3.246

### 4.1.2 Check

    print("lme4:")
    ## [1] "lme4:"
    tmp <- as.data.frame(VarCorr(fits1$lme4))
    tmp <- setNames(tmp$vcov, tmp$grp)
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_error")]),
      estim = tmp[c("GMA", "Residual")]
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim       nBE
    ## var_GMA      10 10.5474529  5.474529
    ## var_error     1  0.9599666 -4.003335

    print("TMB:")
    ## [1] "TMB:"
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_error")]),
      estim = do.call(c, fits1$TMB$report[c("var_GMA", "var_err")])
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim       nBE
    ## var_GMA      10 10.5474516  5.474516
    ## var_error     1  0.9599667 -4.003335

    if ("MM4LMM" %in% names(fits1)) {
      print(fits1$MM4LMM$Sigma2)
    }

## 4.2 Model 2

### 4.2.1 Fit

    form <- pheno2 ~ 1 + block
    listZ <- list(
      "GMA" = allZ$GMA,
      "SMA" = allZ$SMA_mod2
    )
    listVCov <- list(
      "GMA" = GRM,
      "SMA" = diag(ncol(listZ$SMA))
    )
    listContr <- list(block = "contr.sum")
    fits2 <- runAllPkgs(pkgs, form, dat, listZ, listVCov, listContr)
    ## [1] "fit with lme4..."
    ##    user  system elapsed
    ##   1.374   0.000   1.375
    ## [1] "fit with TMB..."
    ##    user  system elapsed
    ##   9.789   0.112   9.901

### 4.2.2 Check

    print("lme4:")
    ## [1] "lme4:"
    tmp <- as.data.frame(VarCorr(fits2$lme4))
    tmp <- setNames(tmp$vcov, tmp$grp)
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA", "var_error")]),
      estim = tmp[c("GMA", "SMA", "Residual")]
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim        nBE
    ## var_GMA      10 10.4978544  4.9785441
    ## var_SMA       2  2.0197144  0.9857177
    ## var_error     1  0.9453828 -5.4617169

    print("TMB:")
    ## [1] "TMB:"
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA", "var_error")]),
      estim = do.call(c, fits2$TMB$report[c("var_GMA", "var_SMA1", "var_err")])
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim        nBE
    ## var_GMA      10 10.4979307  4.9793071
    ## var_SMA       2  2.0197059  0.9852969
    ## var_error     1  0.9453837 -5.4616269

    if ("MM4LMM" %in% names(fits2)) {
      print(fits2$MM4LMM$Sigma2)
    }

## 4.3 Model 3

### 4.3.1 Fit

    form <- pheno3 ~ 1 + block
    listZ <- list(
      "GMA" = allZ$GMA,
      "SMA" = allZ$SMA_mod3
    )
    listVCov <- list(
      "GMA" = GRM,
      "SMA" = diag(ncol(listZ$SMA))
    )
    listContr <- list(block = "contr.sum")
    fits3 <- runAllPkgs(pkgs, form, dat, listZ, listVCov, listContr)
    ## [1] "fit with lme4..."
    ##    user  system elapsed
    ##   1.906   0.012   1.918
    ## [1] "fit with TMB..."
    ##    user  system elapsed
    ##  10.800   0.156  10.957

### 4.3.2 Check

    print("lme4:")
    ## [1] "lme4:"
    tmp <- as.data.frame(VarCorr(fits3$lme4))
    tmp <- setNames(tmp$vcov, tmp$grp)
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA", "var_error")]),
      estim = tmp[c("GMA", "SMA", "Residual")]
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim       nBE
    ## var_GMA      10 10.2611009  2.611009
    ## var_SMA       2  2.1054392  5.271960
    ## var_error     1  0.9469001 -5.309988

    print("TMB:")
    ## [1] "TMB:"
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA", "var_error")]),
      estim = do.call(c, fits3$TMB$report[c("var_GMA", "var_SMA1", "var_err")])
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim       nBE
    ## var_GMA      10 10.2611022  2.611022
    ## var_SMA       2  2.1054387  5.271936
    ## var_error     1  0.9469002 -5.309984

    if ("MM4LMM" %in% names(fits3)) {
      print(fits3$MM4LMM$Sigma2)
    }

## 4.4 Model 2’

### 4.4.1 Fit

    form <- pheno2p ~ 1 + block
    listZ <- list(
      "GMA" = allZ$GMA,
      "SMA" = allZ$SMA_mod2p
    )
    listVCov <- list(
      "GMA" = GRM,
      "SMA" = diag(ncol(listZ$SMA))
    )
    listContr <- list(block = "contr.sum")
    fits2p <- runAllPkgs(pkgs, form, dat, listZ, listVCov, listContr)
    ## [1] "fit with lme4..."
    ## Warning in split.default(x, g): data length is not a multiple of split variable
    ## Warning in split.default(seq_along(x), f, drop = drop, ...): data length is not
    ## a multiple of split variable
    ## Warning in split.default(x, g): data length is not a multiple of split variable
    ## Warning in split.default(seq_along(x), f, drop = drop, ...): data length is not
    ## a multiple of split variable
    ##    user  system elapsed
    ##   1.236   0.000   1.236
    ## [1] "fit with TMB..."
    ##    user  system elapsed
    ##   8.632   0.068   8.700

### 4.4.2 Check

    print("lme4:")
    ## [1] "lme4:"
    tmp <- as.data.frame(VarCorr(fits2p$lme4))
    tmp <- setNames(tmp$vcov, tmp$grp)
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA", "var_error")]),
      estim = tmp[c("GMA", "SMA", "Residual")]
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim        nBE
    ## var_GMA      10 10.3596090   3.596090
    ## var_SMA       2  1.6026602 -19.866989
    ## var_error     1  0.9456708  -5.432918

    print("TMB:")
    ## [1] "TMB:"
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA", "var_error")]),
      estim = do.call(c, fits2p$TMB$report[c("var_GMA", "var_SMA1", "var_err")])
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##           truth      estim        nBE
    ## var_GMA      10 10.3596074   3.596074
    ## var_SMA       2  1.6026609 -19.866954
    ## var_error     1  0.9456708  -5.432919

    if ("MM4LMM" %in% names(fits2p)) {
      print(fits2p$MM4LMM$Sigma2)
    }

## 4.5 Model 2’’

### 4.5.1 Fit

    form <- pheno2pp ~ 1 + block
    listZ <- list(
      "GMA" = allZ$GMA,
      "SMA_ij" = allZ$SMA_mod2pp_ij,
      "SMA_ii" = allZ$SMA_mod2pp_ii
    )
    listVCov <- list(
      "GMA" = GRM,
      "SMA_ij" = diag(ncol(listZ$SMA_ij)),
      "SMA_ii" = diag(ncol(listZ$SMA_ii))
    )
    listContr <- list(block = "contr.sum")
    fits2pp <- runAllPkgs(pkgs, form, dat, listZ, listVCov, listContr)
    ## [1] "fit with lme4..."
    ## Warning in split.default(x, g): data length is not a multiple of split variable
    ## Warning in split.default(seq_along(x), f, drop = drop, ...): data length is not
    ## a multiple of split variable
    ## Warning in split.default(x, g): data length is not a multiple of split variable
    ## Warning in split.default(seq_along(x), f, drop = drop, ...): data length is not
    ## a multiple of split variable
    ##    user  system elapsed
    ##   2.313   0.000   2.314
    ## [1] "fit with TMB..."
    ##    user  system elapsed
    ##   9.726   0.092   9.817

### 4.5.2 Check

    print("lme4:")
    ## [1] "lme4:"
    tmp <- as.data.frame(VarCorr(fits2pp$lme4))
    tmp <- setNames(tmp$vcov, tmp$grp)
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA_ij", "var_SMA_ii", "var_error")]),
      estim = tmp[c("GMA", "SMA_ij", "SMA_ii", "Residual")]
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##            truth      estim         nBE
    ## var_GMA     10.0 10.0082780  0.08278019
    ## var_SMA_ij   1.5  1.6172865  7.81910037
    ## var_SMA_ii   0.8  0.8345874  4.32342341
    ## var_error    1.0  0.9453837 -5.46163448

    print("TMB:")
    ## [1] "TMB:"
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA_ij", "var_SMA_ii", "var_error")]),
      estim = do.call(c, fits2pp$TMB$report[c("var_GMA", "var_SMA1", "var_SMA2", "var_err")])
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##            truth      estim         nBE
    ## var_GMA     10.0 10.0082809  0.08280897
    ## var_SMA_ij   1.5  1.6172866  7.81910639
    ## var_SMA_ii   0.8  0.8345874  4.32341949
    ## var_error    1.0  0.9453836 -5.46163514

    if ("MM4LMM" %in% names(fits2pp)) {
      print(fits2pp$MM4LMM$Sigma2)
    }

## 4.6 Model 3’

### 4.6.1 Fit

    form <- pheno3p ~ 1 + block
    listZ <- list(
      "GMA" = allZ$GMA,
      "SMA_ij" = allZ$SMA_mod3p_ij,
      "SMA_ii" = allZ$SMA_mod3p_ii
    )
    listVCov <- list(
      "GMA" = GRM,
      "SMA_ij" = diag(ncol(listZ$SMA_ij)),
      "SMA_ii" = diag(ncol(listZ$SMA_ii))
    )
    listContr <- list(block = "contr.sum")
    fits3p <- runAllPkgs(pkgs, form, dat, listZ, listVCov, listContr)
    ## [1] "fit with lme4..."
    ## Warning in split.default(x, g): data length is not a multiple of split variable
    ## Warning in split.default(seq_along(x), f, drop = drop, ...): data length is not
    ## a multiple of split variable
    ## Warning in split.default(x, g): data length is not a multiple of split variable
    ## Warning in split.default(seq_along(x), f, drop = drop, ...): data length is not
    ## a multiple of split variable
    ##    user  system elapsed
    ##   3.547   0.000   3.547
    ## [1] "fit with TMB..."
    ##    user  system elapsed
    ##  10.776   0.099  10.895

### 4.6.2 Check

    print("lme4:")
    ## [1] "lme4:"
    tmp <- as.data.frame(VarCorr(fits3p$lme4))
    tmp <- setNames(tmp$vcov, tmp$grp)
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA_ij", "var_SMA_ii", "var_error")]),
      estim = tmp[c("GMA", "SMA_ij", "SMA_ii", "Residual")]
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##            truth      estim        nBE
    ## var_GMA     10.0 10.0390341  0.3903412
    ## var_SMA_ij   1.5  1.6726159 11.5077264
    ## var_SMA_ii   0.8  0.9481598 18.5199796
    ## var_error    1.0  0.9440193 -5.5980736

    print("TMB:")
    ## [1] "TMB:"
    checks <- data.frame(
      truth = do.call(c, truth[c("var_GMA", "var_SMA_ij", "var_SMA_ii", "var_error")]),
      estim = do.call(c, fits3p$TMB$report[c("var_GMA", "var_SMA1", "var_SMA2", "var_err")])
    )
    checks$nBE <- normBiasError(checks$estim, checks$truth)
    print(checks)
    ##            truth      estim        nBE
    ## var_GMA     10.0 10.0390775  0.3907745
    ## var_SMA_ij   1.5  1.6726225 11.5081637
    ## var_SMA_ii   0.8  0.9481763 18.5220407
    ## var_error    1.0  0.9440195 -5.5980522

    if ("MM4LMM" %in% names(fits3p)) {
      print(fits3p$MM4LMM$Sigma2)
    }

# 5 Appendix

    t1 <- proc.time()
    t1 - t0
    ##    user  system elapsed
    ## 161.446   2.630 164.093
    print(sessionInfo(), locale = FALSE)
    ## R version 4.5.1 (2025-06-13)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Debian GNU/Linux 12 (bookworm)
    ##
    ## Matrix products: default
    ## BLAS:   /usr/local/R/4.5.1/lib/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0  LAPACK version 3.11.0
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] plantmix_1.0.3 lme4_1.1-37    Matrix_1.7-3   ggplot2_3.5.2  knitr_1.50
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] gtable_0.3.6       jsonlite_2.0.0     dplyr_1.1.4        compiler_4.5.1
    ##  [5] Rcpp_1.1.0         tidyselect_1.2.1   dichromat_2.0-0.1  jquerylib_0.1.4
    ##  [9] splines_4.5.1      scales_1.4.0       boot_1.3-31        yaml_2.3.10
    ## [13] fastmap_1.2.0      lattice_0.22-7     R6_2.6.1           generics_0.1.4
    ## [17] igraph_2.1.4       rbibutils_2.3      MASS_7.3-65        tibble_3.3.0
    ## [21] nloptr_2.2.1       minqa_1.2.8        TMB_1.9.21         bslib_0.9.0
    ## [25] pillar_1.11.0      RColorBrewer_1.1-3 rlang_1.1.7        cachem_1.1.0
    ## [29] xfun_0.52          sass_0.4.10        cli_3.6.5          withr_3.0.2
    ## [33] magrittr_2.0.3     Rdpack_2.6.4       digest_0.6.37      grid_4.5.1
    ## [37] lifecycle_1.0.5    nlme_3.1-168       reformulas_0.4.4   vctrs_0.6.5
    ## [41] evaluate_1.0.4     glue_1.8.0         farver_2.1.2       rmarkdown_2.29
    ## [45] tools_4.5.1        pkgconfig_2.0.3    htmltools_0.5.8.1