Code
# options to customize chunk outputs
knitr::opts_chunk$set(
tidy.opts = list(width.cutoff = 65),
tidy = TRUE,
message = FALSE
)OLICOCIBI
Predictores del desempeño
Objetivo
- Evaluar predictores del desempeño en la Olimpiada Costarricense de Biología (OLICOCIBI)
Cargar paquetes
Code
# | message: false | warning: false
# install/ load packages
sketchy::load_packages(packages = c("knitr", "formatR", "readxl",
"lme4", "lmerTest", "emmeans", "ggplot2", "viridis"))1 Leer datos
Code
datos_org <- read_excel("/home/m/Dropbox/Projects/olimpiadas_biologia/OLICOCIBI_Total_Recopilado.xlsx")
# dat$ names(dat)
dat <- datos_org[complete.cases(datos_org[, c("Provincia", "Zona",
"Tipo", "Sexo", "Año", "Categoría (A o B)", "Nota eliminatoria")]),
]
dat$Medalla.bin <- ifelse(is.na(dat$Medalla), "No", "Si")
# nrow(dat)
dat <- dat[, c("Provincia", "Zona", "Tipo", "Sexo", "Año", "Categoría (A o B)",
"Nota eliminatoria", "Medalla.bin")]
names(dat) <- c("Provincia", "Zona", "Tipo", "Sexo", "Año", "Categoria",
"Nota", "Medalla.bin")
dat$Sexo[dat$Sexo == "FF"] <- "F"
dat$Sexo[dat$Sexo == "f"] <- "F"
dat$Sexo[dat$Sexo == "m"] <- "M"
dat <- dat[dat$Sexo != "*", ]
# nrow(dat) table(dat$Sexo) table(dat$Tipo)
dat$Notap <- dat$Nota/1002 Analisis
2.1 Nota eliminatoria
2.1.1 Regresión
Code
dat$Provincia <- factor(dat$Provincia, levels = c("San Jose", "Alajuela",
"Heredia", "Cartago", "Limon", "Puntarenas", "Guanacaste"))
formula <- "Notap ~ Provincia + Zona + Tipo + Sexo + Categoria"
mod <- glm(formula, data = dat, family = binomial(link = "logit"))Warning in eval(family$initialize): non-integer #successes in a binomial glm!Code
summary(mod)
Call:
glm(formula = formula, family = binomial(link = "logit"), data = dat)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.03128 0.09184 -0.341 0.73338
ProvinciaAlajuela 0.04898 0.05649 0.867 0.38590
ProvinciaHeredia 0.10768 0.06939 1.552 0.12072
ProvinciaCartago -0.08129 0.08121 -1.001 0.31685
ProvinciaLimon -0.14222 0.07075 -2.010 0.04442 *
ProvinciaPuntarenas -0.12217 0.07239 -1.688 0.09147 .
ProvinciaGuanacaste -0.23189 0.07707 -3.009 0.00262 **
ZonaUrbano 0.03345 0.06124 0.546 0.58487
TipoPNC -0.60411 0.05990 -10.086 < 2e-16 ***
TipoPrivado -0.41933 0.05705 -7.350 1.98e-13 ***
TipoSubvencionado -0.42431 0.09539 -4.448 8.66e-06 ***
SexoM 0.01821 0.03932 0.463 0.64338
CategoriaB 0.09147 0.03923 2.331 0.01973 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1121.74 on 11267 degrees of freedom
Residual deviance: 963.61 on 11255 degrees of freedom
AIC: 13061
Number of Fisher Scoring iterations: 32.1.2 Comparaciones multiples
Code
# contrasts by tipo
emmeans(mod, pairwise ~ Tipo, type = "response")$emmeans
Tipo prob SE df asymp.LCL asymp.UCL
PC 0.495 0.01339 Inf 0.469 0.521
PNC 0.349 0.00774 Inf 0.334 0.364
Privado 0.392 0.01054 Inf 0.371 0.413
Subvencionado 0.391 0.02037 Inf 0.352 0.431
Results are averaged over the levels of: Provincia, Zona, Sexo, Categoria
Confidence level used: 0.95
Intervals are back-transformed from the logit scale
$contrasts
contrast odds.ratio SE df null z.ratio p.value
PC / PNC 1.830 0.1096 Inf 1 10.086 <.0001
PC / Privado 1.521 0.0868 Inf 1 7.350 <.0001
PC / Subvencionado 1.529 0.1458 Inf 1 4.448 0.0001
PNC / Privado 0.831 0.0431 Inf 1 -3.565 0.0021
PNC / Subvencionado 0.835 0.0767 Inf 1 -1.959 0.2035
Privado / Subvencionado 1.005 0.0877 Inf 1 0.057 0.9999
Results are averaged over the levels of: Provincia, Zona, Sexo, Categoria
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log odds ratio scale 2.1.3 Gráficos
Code
dat$Tipo <- factor(dat$Tipo, levels = c("PC", "Privado", "Subvencionado", "PNC"))
# raincoud plot:
fill_color <- mako(10, alpha = 0.4)[7]
ggplot(dat, aes(y = Nota, x = Tipo)) +
# add half-violin from {ggdist} package
ggdist::stat_halfeye(
fill = fill_color,
alpha = 0.5,
# custom bandwidth
adjust = .5,
# adjust height
width = .6,
.width = 0,
# move geom to the cright
justification = -.2,
point_colour = NA
) +
geom_boxplot(fill = fill_color,
width = .15,
# remove outliers
outlier.shape = NA # `outlier.shape = NA` works as well
) +
# add justified jitter from the {gghalves} package
gghalves::geom_half_point(
color = fill_color,
# draw jitter on the left
side = "l",
# control range of jitter
range_scale = .4,
# add some transparency
alpha = .5,
) +
# ylim(c(-30, 310)) +
# geom_text(data = agg_dat, aes(y = rep(-25, 5), x = sensory_input, label = n.labels), nudge_x = -0.13, size = 6) +
# scale_x_discrete(labels=c("Control" = "Noise control", "Sound vision" = "Sound & vision", "Vision" = "Vision", "Lessen input" = "Lessen input")) +
labs(x = "Tipo de colegio", y = "Nota (%)") + theme(axis.text.x = element_text(angle = 15, hjust = 1))
Code
agg <- aggregate(Notap ~ Provincia, data = dat, mean)
agg <- agg[order(agg$Notap, decreasing = T),]
dat$Provincia <- factor(dat$Provincia, levels = agg$Provincia)
ggplot(dat, aes(y = Nota, x = Provincia)) +
# add half-violin from {ggdist} package
ggdist::stat_halfeye(
fill = fill_color,
alpha = 0.5,
# custom bandwidth
adjust = .5,
# adjust height
width = .6,
.width = 0,
# move geom to the cright
justification = -.2,
point_colour = NA
) +
geom_boxplot(fill = fill_color,
width = .15,
# remove outliers
outlier.shape = NA # `outlier.shape = NA` works as well
) +
# add justified jitter from the {gghalves} package
gghalves::geom_half_point(
color = fill_color,
# draw jitter on the left
side = "l",
# control range of jitter
range_scale = .4,
# add some transparency
alpha = .5,
) +
# ylim(c(-30, 310)) +
# geom_text(data = agg_dat, aes(y = rep(-25, 5), x = sensory_input, label = n.labels), nudge_x = -0.13, size = 6) +
# scale_x_discrete(labels=c("Control" = "Noise control", "Sound vision" = "Sound & vision", "Vision" = "Vision", "Lessen input" = "Lessen input")) +
labs(x = "Provincia", y = "Nota (%)") + theme(axis.text.x = element_text(angle = 15, hjust = 1))
2.2 Medallas
2.2.1 Regresión
Code
dat$Provincia <- factor(dat$Provincia, levels = c("San Jose", "Alajuela",
"Heredia", "Cartago", "Limon", "Puntarenas", "Guanacaste"))
dat$medalla <- ifelse(dat$Medalla.bin == "Si", 1, 0)
formula <- "medalla ~ Provincia + Zona + Tipo + Sexo + Categoria"
mod <- glm(formula, data = dat, family = binomial(link = "logit"))
summary(mod)
Call:
glm(formula = formula, family = binomial(link = "logit"), data = dat)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.02055 0.24579 -8.220 < 2e-16 ***
ProvinciaAlajuela 0.22167 0.10851 2.043 0.04106 *
ProvinciaHeredia 0.20663 0.15772 1.310 0.19016
ProvinciaCartago -0.59420 0.19720 -3.013 0.00259 **
ProvinciaLimon -0.96287 0.17928 -5.371 7.84e-08 ***
ProvinciaPuntarenas -0.73822 0.18502 -3.990 6.61e-05 ***
ProvinciaGuanacaste -2.19322 0.31686 -6.922 4.46e-12 ***
ZonaUrbano 0.49134 0.20901 2.351 0.01874 *
TipoPrivado -1.53743 0.10379 -14.812 < 2e-16 ***
TipoSubvencionado -2.07364 0.24290 -8.537 < 2e-16 ***
TipoPNC -2.43238 0.15425 -15.769 < 2e-16 ***
SexoM 0.42719 0.08618 4.957 7.15e-07 ***
CategoriaB 0.05307 0.08671 0.612 0.54050
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 4857.9 on 11267 degrees of freedom
Residual deviance: 4165.6 on 11255 degrees of freedom
AIC: 4191.6
Number of Fisher Scoring iterations: 72.2.2 Comparaciones multiples
Code
# contrasts by tipo
emmeans(mod, pairwise ~ Tipo, type = "response")$emmeans
Tipo prob SE df asymp.LCL asymp.UCL
PC 0.1077 0.01200 Inf 0.08631 0.1335
Privado 0.0253 0.00324 Inf 0.01965 0.0325
Subvencionado 0.0149 0.00370 Inf 0.00918 0.0242
PNC 0.0105 0.00147 Inf 0.00797 0.0138
Results are averaged over the levels of: Provincia, Zona, Sexo, Categoria
Confidence level used: 0.95
Intervals are back-transformed from the logit scale
$contrasts
contrast odds.ratio SE df null z.ratio p.value
PC / Privado 4.65 0.483 Inf 1 14.812 <.0001
PC / Subvencionado 7.95 1.932 Inf 1 8.537 <.0001
PC / PNC 11.39 1.756 Inf 1 15.769 <.0001
Privado / Subvencionado 1.71 0.407 Inf 1 2.251 0.1097
Privado / PNC 2.45 0.382 Inf 1 5.735 <.0001
Subvencionado / PNC 1.43 0.383 Inf 1 1.342 0.5358
Results are averaged over the levels of: Provincia, Zona, Sexo, Categoria
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log odds ratio scale 2.2.3 Gráficos
Code
dat$Tipo <- factor(dat$Tipo, levels = c("PC", "Privado", "Subvencionado",
"PNC"))
# raincoud plot:
fill_color <- mako(10, alpha = 0.7)[7]
ggplot(dat, aes(y = medalla, x = Tipo)) + geom_bar(stat = "identity",
fill = fill_color) + labs(x = "Tipo de colegio", y = "Total de medallas") +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
Code
agg_med <- aggregate(medalla ~ Tipo, data = dat, function(x) sum(x)/length(x))
ggplot(agg_med, aes(y = medalla, x = Tipo)) + geom_bar(stat = "identity",
fill = fill_color) + labs(x = "Tipo de colegio", y = "Proporción con medalla") +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
Code
ggplot(dat, aes(y = medalla, x = Provincia)) + geom_bar(stat = "identity",
fill = fill_color) + labs(x = "Tipo de colegio", y = "Total de medallas") +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
Code
agg_med <- aggregate(medalla ~ Provincia, data = dat, function(x) sum(x)/length(x))
agg_med <- agg_med[order(agg_med$medalla, decreasing = T), ]
agg_med$Provincia <- factor(agg_med$Provincia, levels = agg_med$Provincia)
ggplot(agg_med, aes(y = medalla, x = Provincia)) + geom_bar(stat = "identity",
fill = fill_color) + labs(x = "Provincia", y = "Proporción con medalla") +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
2.3 Nota final
2.3.1 Regresión
Code
# | message: false | warning: false
dat <- datos_org[complete.cases(datos_org[, c("Provincia", "Zona",
"Tipo", "Sexo", "Año", "Categoría (A o B)", "Nota final")]),
]
dat$Medalla.bin <- ifelse(is.na(dat$Medalla), "No", "Si")
dat <- dat[, c("Provincia", "Zona", "Tipo", "Sexo", "Año", "Categoría (A o B)",
"Nota final")]
names(dat) <- c("Provincia", "Zona", "Tipo", "Sexo", "Año", "Categoria",
"Nota")
dat$Sexo[dat$Sexo == "FF"] <- "F"
dat$Sexo[dat$Sexo == "f"] <- "F"
dat$Sexo[dat$Sexo == "m"] <- "M"
dat <- dat[dat$Sexo != "*", ]
# nrow(dat) table(dat$Sexo) table(dat$Tipo)
dat$Notap <- dat$Nota/100Code
dat$Provincia <- factor(dat$Provincia, levels = c("San Jose", "Alajuela",
"Heredia", "Cartago", "Limon", "Puntarenas", "Guanacaste"))
formula <- "Notap ~ Provincia + Zona + Tipo + Sexo + Categoria"
mod <- glm(formula, data = dat, family = binomial(link = "logit"))Warning in eval(family$initialize): non-integer #successes in a binomial glm!Code
summary(mod)
Call:
glm(formula = formula, family = binomial(link = "logit"), data = dat)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.34989 0.25388 1.378 0.1681
ProvinciaAlajuela -0.04806 0.12810 -0.375 0.7075
ProvinciaHeredia 0.06093 0.17691 0.344 0.7305
ProvinciaCartago -0.01244 0.25353 -0.049 0.9609
ProvinciaLimon -0.10219 0.20212 -0.506 0.6132
ProvinciaPuntarenas -0.09991 0.20364 -0.491 0.6237
ProvinciaGuanacaste -0.32065 0.29438 -1.089 0.2761
ZonaUrbano -0.08098 0.20364 -0.398 0.6909
TipoPNC -0.32917 0.16311 -2.018 0.0436 *
TipoPrivado -0.16413 0.12125 -1.354 0.1758
TipoSubvencionado -0.38659 0.26142 -1.479 0.1392
SexoM 0.10479 0.09954 1.053 0.2925
CategoriaB -0.44610 0.10150 -4.395 1.11e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 127.817 on 1712 degrees of freedom
Residual deviance: 98.611 on 1700 degrees of freedom
AIC: 2217.9
Number of Fisher Scoring iterations: 32.3.2 Comparaciones multiples
Code
# contrasts by tipo
emmeans(mod, pairwise ~ Tipo, type = "response")$emmeans
Tipo prob SE df asymp.LCL asymp.UCL
PC 0.516 0.0311 Inf 0.455 0.576
PNC 0.434 0.0332 Inf 0.371 0.500
Privado 0.475 0.0329 Inf 0.411 0.540
Subvencionado 0.420 0.0632 Inf 0.303 0.546
Results are averaged over the levels of: Provincia, Zona, Sexo, Categoria
Confidence level used: 0.95
Intervals are back-transformed from the logit scale
$contrasts
contrast odds.ratio SE df null z.ratio p.value
PC / PNC 1.390 0.227 Inf 1 2.018 0.1812
PC / Privado 1.178 0.143 Inf 1 1.354 0.5286
PC / Subvencionado 1.472 0.385 Inf 1 1.479 0.4503
PNC / Privado 0.848 0.138 Inf 1 -1.017 0.7395
PNC / Subvencionado 1.059 0.293 Inf 1 0.208 0.9968
Privado / Subvencionado 1.249 0.321 Inf 1 0.867 0.8221
Results are averaged over the levels of: Provincia, Zona, Sexo, Categoria
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log odds ratio scale 2.3.3 Gráficos
Code
dat$Tipo <- factor(dat$Tipo, levels = c("PC", "Privado", "Subvencionado", "PNC"))
# raincoud plot:
fill_color <- mako(10, alpha = 0.4)[7]
ggplot(dat, aes(y = Nota, x = Tipo)) +
# add half-violin from {ggdist} package
ggdist::stat_halfeye(
fill = fill_color,
alpha = 0.5,
# custom bandwidth
adjust = .5,
# adjust height
width = .6,
.width = 0,
# move geom to the cright
justification = -.2,
point_colour = NA
) +
geom_boxplot(fill = fill_color,
width = .15,
# remove outliers
outlier.shape = NA # `outlier.shape = NA` works as well
) +
# add justified jitter from the {gghalves} package
gghalves::geom_half_point(
color = fill_color,
# draw jitter on the left
side = "l",
# control range of jitter
range_scale = .4,
# add some transparency
alpha = .5,
) +
# ylim(c(-30, 310)) +
# geom_text(data = agg_dat, aes(y = rep(-25, 5), x = sensory_input, label = n.labels), nudge_x = -0.13, size = 6) +
# scale_x_discrete(labels=c("Control" = "Noise control", "Sound vision" = "Sound & vision", "Vision" = "Vision", "Lessen input" = "Lessen input")) +
labs(x = "Tipo de colegio", y = "Nota (%)") + theme(axis.text.x = element_text(angle = 15, hjust = 1))
Code
agg <- aggregate(Notap ~ Provincia, data = dat, mean)
agg <- agg[order(agg$Notap, decreasing = T),]
dat$Provincia <- factor(dat$Provincia, levels = agg$Provincia)
ggplot(dat, aes(y = Nota, x = Provincia)) +
# add half-violin from {ggdist} package
ggdist::stat_halfeye(
fill = fill_color,
alpha = 0.5,
# custom bandwidth
adjust = .5,
# adjust height
width = .6,
.width = 0,
# move geom to the cright
justification = -.2,
point_colour = NA
) +
geom_boxplot(fill = fill_color,
width = .15,
# remove outliers
outlier.shape = NA # `outlier.shape = NA` works as well
) +
# add justified jitter from the {gghalves} package
gghalves::geom_half_point(
color = fill_color,
# draw jitter on the left
side = "l",
# control range of jitter
range_scale = .4,
# add some transparency
alpha = .5,
) +
# ylim(c(-30, 310)) +
# geom_text(data = agg_dat, aes(y = rep(-25, 5), x = sensory_input, label = n.labels), nudge_x = -0.13, size = 6) +
# scale_x_discrete(labels=c("Control" = "Noise control", "Sound vision" = "Sound & vision", "Vision" = "Vision", "Lessen input" = "Lessen input")) +
labs(x = "Provincia", y = "Nota (%)") + theme(axis.text.x = element_text(angle = 15, hjust = 1))
Información de la sesión
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.2 (2024-10-31)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Costa_Rica
date 2025-02-21
pandoc 3.1.1 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
boot 1.3-30 2024-02-26 [1] CRAN (R 4.4.1)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.1)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.1)
cli 3.6.4 2025-02-13 [1] CRAN (R 4.4.2)
coda 0.19-4.1 2024-01-31 [1] CRAN (R 4.4.1)
codetools 0.2-20 2024-03-31 [1] CRAN (R 4.4.1)
colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.1)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.1)
devtools 2.4.5 2022-10-11 [1] CRAN (R 4.4.1)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
distributional 0.5.0 2024-09-17 [1] CRAN (R 4.4.1)
dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.4.1)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.1)
emmeans * 1.10.3 2024-07-01 [1] CRAN (R 4.4.1)
estimability 1.5.1 2024-05-12 [1] CRAN (R 4.4.1)
evaluate 1.0.3 2025-01-10 [1] CRAN (R 4.4.2)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.1)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.1)
formatR * 1.14 2023-01-17 [1] CRAN (R 4.4.1)
fs 1.6.5 2024-10-30 [1] CRAN (R 4.4.1)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.1)
ggdist 3.3.2 2024-03-05 [1] CRAN (R 4.4.1)
gghalves 0.1.4 2022-11-20 [1] CRAN (R 4.4.1)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.1)
glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.4.1)
gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.1)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.1)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.1)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.1)
jsonlite 1.8.9 2024-09-20 [1] CRAN (R 4.4.1)
knitr * 1.49 2024-11-08 [1] CRAN (R 4.4.1)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.1)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.1)
lattice 0.22-6 2024-03-20 [1] CRAN (R 4.4.1)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.1)
lme4 * 1.1-35.5 2024-07-03 [1] CRAN (R 4.4.1)
lmerTest * 3.1-3 2020-10-23 [1] CRAN (R 4.4.1)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.1)
MASS 7.3-61 2024-06-13 [1] CRAN (R 4.4.1)
Matrix * 1.7-0 2024-04-26 [1] CRAN (R 4.4.1)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.1)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.1)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.1)
minqa 1.2.7 2024-05-20 [1] CRAN (R 4.4.1)
multcomp 1.4-25 2023-06-20 [1] CRAN (R 4.4.1)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.1)
mvtnorm 1.3-1 2024-09-03 [1] CRAN (R 4.4.1)
nlme 3.1-165 2024-06-06 [1] CRAN (R 4.4.1)
nloptr 2.1.1 2024-06-25 [1] CRAN (R 4.4.1)
numDeriv 2016.8-1.1 2019-06-06 [1] CRAN (R 4.4.1)
packrat 0.9.2 2023-09-05 [1] CRAN (R 4.4.1)
pillar 1.10.1 2025-01-07 [1] CRAN (R 4.4.2)
pkgbuild 1.4.6 2025-01-16 [1] CRAN (R 4.4.2)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.1)
pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.4.1)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.4.1)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.1)
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[4] /usr/lib/R/library
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