Cortisol Concentration Values, Test4
Paloma Contreras
2025-04-09
Last updated: 2025-04-09
Checks: 6 1
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HairCort-Evaluation-Nist2020/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 77c2ab5 | Paloma | 2025-04-08 | cleaning test3 |
| Rmd | ced6eed | Paloma | 2025-04-03 | upd |
| html | ced6eed | Paloma | 2025-04-03 | upd |
| Rmd | ca6c804 | Paloma | 2025-04-03 | new calc final vals |
| html | ca6c804 | Paloma | 2025-04-03 | new calc final vals |
| Rmd | 528855b | Paloma | 2025-04-03 | new_calc |
| html | 528855b | Paloma | 2025-04-03 | new_calc |
Summary
Cortisol value calculations
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | NA’s | |
|---|---|---|---|---|---|---|---|
| A) Standard Method | 0.4142 | 1.9653 | 7.5533 | 14.1906 | 28.0667 | 50.933 | 3 |
| B) Spike-Corrected Method | -45.870 | -31.922 | -1.929 | -9.444 | 7.553 | 30.780 | 3 |
| C) Standard, but simplified equation | 0.4142 | 1.9653 | 7.5533 | 14.1906 | 28.0667 | 50.9333 | 3 |
| D) Spike-Corrected (divided by two) | 0.2335 | 1.5067 | 6.7184 | 9.7699 | 17.3683 | 32.2815 | 3 |
I calculated final cortisol values in pg/mg using the following key variables:
Ave_Conc_pg/ml: average ELISA reading per sample in pg/mL
Weight_mg: hair weight in mg
Buffer_nl: assay buffer volume in nL → we convert to mL
Spike: binary indicator (1 = spiked sample)
SpikeVol_uL: volume of spike added in µL
Dilution: dilution factor (already present)
Vol_in_well.tube_uL: total volume in well/tube in µL (for spike correction)
std: standard reading value
extraction: methanol volume ratio = vol added / vol recovered (1/0.75 ml)
Results:
Intra-assay CV: 14.5%
Intra-assay CV after removing low quality samples: 10%
Inter-assay CV: 21%
(Bindings for 20mg sample diluted in 250 uL, no spike: 64.8% and 48% in test3 and test4, respectively)
Conclusions:
Concerns: Overall quality of the plate is not great, but serial dilusions show clear parallelism and standards have values within the expected
Cortisol concentration calculations
# set reading value of spike (std1, 0.333 ug/dL),
# and transforming to ug.dL
std <- (3191+3228)/2
std.r <- (std/10000)
std[1] 3209.5std.r[1] 0.32095# according to chatgpt, the spike's contribution is
# 1600 pg/mL, which is very similar to half of the reading for std 1 :]Loading files and transforming units, including low quality data
df <- read.csv(file.path(data_path,"Data_QC_flagged.csv"))
kable(tail(df))| Wells | Sample | Category | Weight_mg | Buffer_nl | Spike | SpikeVol_uL | Dilution_sample | Dilution_spike | Vol_in_well.tube_uL | Raw.OD | Extraction_ratio | Binding.Perc | Conc_pg.ml | Ave_Conc_pg.ml | CV.Perc | SD | SEM | CV_categ | Binding.Perc_categ | Failed_samples | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 77 | G11 | TP3A | P | 12 | 220 | 1 | 25 | 1 | 1 | 50 | 1.351351 | 0.258 | 23.2 | 2800 | 2792 | 0.391 | 10.9 | 7.71 | NA | NA | NA |
| 78 | H11 | TP3A | P | 12 | 220 | 1 | 25 | 1 | 1 | 50 | 1.351351 | 0.259 | NA | 2785 | NA | NA | NA | NA | NA | NA | NA |
| 79 | A12 | TP3B | P | 12 | 60 | 1 | 25 | 1 | 1 | 50 | 1.333333 | 0.195 | 15.5 | 4084 | 4210 | 4.230 | 178.0 | 126.00 | NA | UNDER 20% binding | UNDER 20% binding |
| 80 | B12 | TP3B | P | 12 | 60 | 1 | 25 | 1 | 1 | 50 | 1.333333 | 0.186 | NA | 4336 | NA | NA | NA | NA | NA | NA | NA |
| 81 | C12 | TP3C | P | 12 | 60 | 1 | 25 | 1 | 1 | 50 | 1.333333 | 0.186 | 13.9 | 4336 | 4661 | 9.870 | 460.0 | 325.00 | NA | UNDER 20% binding | UNDER 20% binding |
| 82 | D12 | TP3C | P | 12 | 60 | 1 | 25 | 1 | 1 | 50 | 1.333333 | 0.166 | NA | 4986 | NA | NA | NA | NA | NA | NA | NA |
# remove outlier
#df<- df[(df$Sample != "TP3A"),]
# Creating variables in indicated units
# dilution (buffer)
df$Buffer_ml <- c(df$Buffer_nl/1000)
# remove unnecessary information
data <- df %>%
dplyr::select(Wells, Sample, Category, Binding.Perc, Ave_Conc_pg.ml, Weight_mg, Buffer_ml, Spike, SpikeVol_uL, Dilution_sample, Dilution_spike, Extraction_ratio, Vol_in_well.tube_uL, Failed_samples)
# remove duplicates
data <- data[!is.na(data$Binding.Perc), ]
kable(tail(data, 10))| Wells | Sample | Category | Binding.Perc | Ave_Conc_pg.ml | Weight_mg | Buffer_ml | Spike | SpikeVol_uL | Dilution_sample | Dilution_spike | Extraction_ratio | Vol_in_well.tube_uL | Failed_samples | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 63 | A10 | TD7 | D | 99.0 | 86.73 | 20 | 0.11 | 1 | 110 | 64 | 64 | 0.934 | 220 | ABOVE 80% binding |
| 65 | C10 | TP1A | P | 21.8 | 2986.00 | 6 | 0.06 | 1 | 25 | 1 | 1 | 0.245 | 50 | NA |
| 67 | E10 | TP1B | P | 18.1 | 3820.00 | 6 | 0.06 | 1 | 25 | 1 | 1 | 0.254 | 50 | HIGH CV;UNDER 20% binding |
| 69 | G10 | TP1C*FAIL | P | 27.8 | 2242.00 | 6 | 0.06 | 1 | 25 | 1 | 1 | 0.305 | 50 | NA |
| 71 | A11 | TP2A | P | 17.3 | 3793.00 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.208 | 50 | UNDER 20% binding |
| 73 | C11 | TP2B | P | 21.1 | 3101.00 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.236 | 50 | NA |
| 75 | E11 | TP2C | P | 18.1 | 3634.00 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.219 | 50 | UNDER 20% binding |
| 77 | G11 | TP3A | P | 23.2 | 2792.00 | 12 | 0.22 | 1 | 25 | 1 | 1 | 0.258 | 50 | NA |
| 79 | A12 | TP3B | P | 15.5 | 4210.00 | 12 | 0.06 | 1 | 25 | 1 | 1 | 0.195 | 50 | UNDER 20% binding |
| 81 | C12 | TP3C | P | 13.9 | 4661.00 | 12 | 0.06 | 1 | 25 | 1 | 1 | 0.186 | 50 | UNDER 20% binding |
dim(data)[1] 41 14(A) Standard Calculation
Formula:
((A/B) * (C/D) * E * 10,000) = F
- A = μg/dl from assay output;
- B = weight (in mg) of hair subjected to extraction;
- C = vol. (in ml) of methanol added to the powdered hair;
- D = vol. (in ml) of methanol recovered from the extract and subsequently dried down;
- E = vol. (in ml) of assay buffer used to reconstitute the dried extract;
- F = final value of hair CORT Concentration in pg/mg.
##################################
##### Calculate final values #####
##################################
# Transform to μg/dl from assay output
data$Ave_Conc_ug.dL <- c(data$Ave_Conc_pg.ml/10000)
data$Final_conc_pg.mg <- c(
((data$Ave_Conc_ug.dL) / data$Weight_mg) * # A/B *
data$Extraction_ratio * # C/D *
data$Buffer_ml * 10000 * data$Dilution_sample) # E * 10000
data <- data[order(data$Sample),]
write.csv(data, file.path(data_path, "Data_cort_values_methodA.csv"), row.names = F)
# summary for all samples
summary(data$Final_conc_pg.mg) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
3.441 6.122 9.825 12.826 16.137 48.522 3 kable(tail(data, 7))| Wells | Sample | Category | Binding.Perc | Ave_Conc_pg.ml | Weight_mg | Buffer_ml | Spike | SpikeVol_uL | Dilution_sample | Dilution_spike | Extraction_ratio | Vol_in_well.tube_uL | Failed_samples | Ave_Conc_ug.dL | Final_conc_pg.mg | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 69 | G10 | TP1C*FAIL | P | 27.8 | 2242 | 6 | 0.06 | 1 | 25 | 1 | 1 | 0.305 | 50 | NA | 0.2242 | 6.838100 |
| 71 | A11 | TP2A | P | 17.3 | 3793 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.208 | 50 | UNDER 20% binding | 0.3793 | 5.259627 |
| 73 | C11 | TP2B | P | 21.1 | 3101 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.236 | 50 | NA | 0.3101 | 4.878907 |
| 75 | E11 | TP2C | P | 18.1 | 3634 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.219 | 50 | UNDER 20% binding | 0.3634 | 5.305640 |
| 77 | G11 | TP3A | P | 23.2 | 2792 | 12 | 0.22 | 1 | 25 | 1 | 1 | 0.258 | 50 | NA | 0.2792 | 13.206160 |
| 79 | A12 | TP3B | P | 15.5 | 4210 | 12 | 0.06 | 1 | 25 | 1 | 1 | 0.195 | 50 | UNDER 20% binding | 0.4210 | 4.104750 |
| 81 | C12 | TP3C | P | 13.9 | 4661 | 12 | 0.06 | 1 | 25 | 1 | 1 | 0.186 | 50 | UNDER 20% binding | 0.4661 | 4.334730 |
dim(data)[1] 41 16(B) Accounting for Spike
We followed the procedure described in Nist et al. 2020:
“Thus, after pipetting 25μL of standards and samples into the appropriate wells of the 96-well assay plate, we added 25μL of the 0.333ug/dL standard to all samples, resulting in a 1:2 dilution of samples. The remainder of the manufacturer’s protocol was unchanged. We analyzed the assay plate in a Powerwave plate reader (BioTek, Winooski, VT) at 450nm and subtracted background values from all assay wells. In the calculations, we subtracted the 0.333ug/dL standard reading from the sample readings. Samples that resulted in a negative number were considered nondetectable. We converted cortisol levels from ug/dL, as measured by the assay, to pg/mg—based on the mass of hair collected and analyzed using the following formula:
A/B * C/D * E * 10,000 * 2 = F
where - A = μg/dl from assay output; - B = weight (in mg) of collected hair; - C = vol. (in ml) of methanol added to the powdered hair; - D = vol. (in ml) of methanol recovered from the extract and subsequently dried down; - E = vol. (in ml) of assay buffer used to reconstitute the dried extract; 10,000 accounts for changes in metrics; 2 accounts for the dilution factor after addition of the spike; and - F = final value of hair cortisol concentration in pg/mg”
dSpike <- data
##################################
##### Calculate final values #####
##################################
dSpike$Final_conc_pg.mg <-
ifelse(
dSpike$Spike == 1, ## Only spiked samples
((dSpike$Ave_Conc_ug.dL - (std.r)) / # (A-spike) / B
dSpike$Weight_mg)
* data$Extraction_ratio * # C / D
dSpike$Buffer_ml * 10000 * 2 * dSpike$Dilution_sample, # E * 10000 * 2
dSpike$Final_conc_pg.mg
)
write.csv(dSpike, file.path(data_path, "Data_cort_values_methodB.csv"), row.names = F)
# summary for all samples
summary(dSpike$Final_conc_pg.mg) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-2053.3337 -43.5050 -0.7183 -171.2293 5.4869 32.3016 3 dSpikeSub <- data[c(data$Spike == 0), ]
summary(dSpikeSub$Final_conc_pg.mg) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
3.948 8.839 13.992 15.577 19.936 32.302 3 kable(tail(dSpike, 10))| Wells | Sample | Category | Binding.Perc | Ave_Conc_pg.ml | Weight_mg | Buffer_ml | Spike | SpikeVol_uL | Dilution_sample | Dilution_spike | Extraction_ratio | Vol_in_well.tube_uL | Failed_samples | Ave_Conc_ug.dL | Final_conc_pg.mg | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 63 | A10 | TD7 | D | 99.0 | 86.73 | 20 | 0.11 | 1 | 110 | 64 | 64 | 0.934 | 220 | ABOVE 80% binding | 0.008673 | -2053.3336947 |
| 65 | C10 | TP1A | P | 21.8 | 2986.00 | 6 | 0.06 | 1 | 25 | 1 | 1 | 0.245 | 50 | NA | 0.298600 | -1.0951500 |
| 67 | E10 | TP1B | P | 18.1 | 3820.00 | 6 | 0.06 | 1 | 25 | 1 | 1 | 0.254 | 50 | HIGH CV;UNDER 20% binding | 0.382000 | 3.1013400 |
| 69 | G10 | TP1C*FAIL | P | 27.8 | 2242.00 | 6 | 0.06 | 1 | 25 | 1 | 1 | 0.305 | 50 | NA | 0.224200 | -5.9017500 |
| 71 | A11 | TP2A | P | 17.3 | 3793.00 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.208 | 50 | UNDER 20% binding | 0.379300 | 1.6182400 |
| 73 | C11 | TP2B | P | 21.1 | 3101.00 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.236 | 50 | NA | 0.310100 | -0.3414133 |
| 75 | E11 | TP2C | P | 18.1 | 3634.00 | 9 | 0.06 | 1 | 25 | 1 | 1 | 0.219 | 50 | UNDER 20% binding | 0.363400 | 1.2395400 |
| 77 | G11 | TP3A | P | 23.2 | 2792.00 | 12 | 0.22 | 1 | 25 | 1 | 1 | 0.258 | 50 | NA | 0.279200 | -3.9495500 |
| 79 | A12 | TP3B | P | 15.5 | 4210.00 | 12 | 0.06 | 1 | 25 | 1 | 1 | 0.195 | 50 | UNDER 20% binding | 0.421000 | 1.9509750 |
| 81 | C12 | TP3C | P | 13.9 | 4661.00 | 12 | 0.06 | 1 | 25 | 1 | 1 | 0.186 | 50 | UNDER 20% binding | 0.466100 | 2.6997900 |
(C) Skip unit transformation
##################################
##### Calculate final values #####
##################################
datac <- data
datac$Final_conc_pg.mg <- c(
(datac$Ave_Conc_pg.ml / datac$Weight_mg) * # A/B *
data$Extraction_ratio * # C/D *
datac$Buffer_ml * datac$Dilution_sample) # E
datac <- datac[order(datac$Sample),]
write.csv(datac, file.path(data_path, "Data_cort_values_methodC.csv"), row.names = F)
# summary for all samples
summary(datac$Final_conc_pg.mg) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
3.441 6.122 9.825 12.826 16.137 48.522 3 (D) Account for Spike contribution (uses vol. of spike, conc. of spike, and total reconstit. vol.)
Spike contribution (pg/mL) = (Vol. spike (mL) x Conc. spike (pg/mL) ) / Vol. reconstitution (mL) or total vol. in well (50uL) (depending on where the spike was added)
# Calculate contribution of spike according to the different volumes in which it was added
# Consider that contribution of spike in serial dilutions gets smaller
# Vol. of spike transformed to mL
data$SpikeVol_ml <- data$SpikeVol_uL/1000
# Concentration of the spike:
std[1] 3209.5# Vol. reconstitution (mL) is the total volume in tube or well (sample + spike), after adding spike.
# transform to mL
data$Vol_in_well.tube_ml <- data$Vol_in_well.tube_uL/1000
##( Spike vol. x Spike Conc.)
## ------------------------ / dilution = Spike contribution
## Total vol.
# Cortisol added by spike in wells: 0.0025 mL x 3200 pg/mL = 80 pg
# Calculate cort contribution of spike to each sample
data$Spike.cont_pg.mL <- (((data$SpikeVol_ml * std ) / # Volume of spike * Spike concentration
data$Vol_in_well.tube_ml) / # divided by the total volume (spike + sample)
data$Dilution_spike) # resulting number changes depending on the dilution
dSpiked <- data
##################################
##### Calculate final values #####
##################################
dSpiked$Final_conc_pg.mg <-
ifelse(
dSpiked$Spike == 1, ## Only spiked samples
((dSpiked$Ave_Conc_pg.ml - dSpiked$Spike.cont_pg.mL) / # (A - spike) / B
dSpiked$Weight_mg)
* data$Extraction_ratio * # C / D
dSpiked$Buffer_ml * dSpiked$Dilution_sample, # E *
dSpiked$Final_conc_pg.mg
)
write.csv(dSpiked, file.path(data_path, "Data_cort_values_methodD.csv"), row.names = F)
# summary for all samples
summary(dSpiked$Final_conc_pg.mg) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.944 3.804 7.125 10.586 13.478 47.271 3 kable(tail(dSpiked[!is.na(dSpiked$Final_conc_pg.mg) , c("Sample", "Final_conc_pg.mg", "Ave_Conc_pg.ml", "Spike.cont_pg.mL", "Binding.Perc", "Weight_mg", "Buffer_ml", "SpikeVol_uL", "Dilution_sample", "Dilution_spike", "Vol_in_well.tube_uL")],20))| Sample | Final_conc_pg.mg | Ave_Conc_pg.ml | Spike.cont_pg.mL | Binding.Perc | Weight_mg | Buffer_ml | SpikeVol_uL | Dilution_sample | Dilution_spike | Vol_in_well.tube_uL | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | TC4 | 7.503227 | 618.00 | 291.77273 | 60.2 | 50 | 0.25 | 25 | 8 | 1 | 275 |
| 45 | TC5 | 9.733520 | 144.50 | 0.00000 | 92.0 | 50 | 0.25 | 0 | 16 | 1 | 250 |
| 47 | TC6 | 13.991765 | 97.49 | 0.00000 | 97.7 | 50 | 0.25 | 0 | 32 | 1 | 250 |
| 49 | TC7 | 32.301600 | 107.50 | 0.00000 | 96.4 | 50 | 0.25 | 0 | 64 | 1 | 250 |
| 51 | TD1 | 2.650400 | 4168.00 | 1604.75000 | 15.7 | 20 | 0.11 | 110 | 1 | 1 | 220 |
| 53 | TD2 | 3.772350 | 1814.00 | 802.37500 | 32.7 | 20 | 0.11 | 110 | 2 | 2 | 220 |
| 55 | TD3 | 6.449542 | 1033.00 | 401.18750 | 46.7 | 20 | 0.11 | 110 | 4 | 4 | 220 |
| 57 | TD4 | 8.852531 | 525.10 | 200.59375 | 64.3 | 20 | 0.11 | 110 | 8 | 8 | 220 |
| 59 | TD5 | 11.171711 | 268.00 | 100.29688 | 80.2 | 20 | 0.11 | 110 | 16 | 16 | 220 |
| 61 | TD6 | 5.262264 | 81.99 | 50.14844 | 99.7 | 20 | 0.11 | 110 | 32 | 32 | 220 |
| 63 | TD7 | 20.270448 | 86.73 | 25.07422 | 99.0 | 20 | 0.11 | 110 | 64 | 64 | 220 |
| 65 | TP1A | 3.384063 | 2986.00 | 1604.75000 | 21.8 | 6 | 0.06 | 25 | 1 | 1 | 50 |
| 67 | TP1B | 5.626735 | 3820.00 | 1604.75000 | 18.1 | 6 | 0.06 | 25 | 1 | 1 | 50 |
| 69 | TP1C*FAIL | 1.943612 | 2242.00 | 1604.75000 | 27.8 | 6 | 0.06 | 25 | 1 | 1 | 50 |
| 71 | TP2A | 3.034373 | 3793.00 | 1604.75000 | 17.3 | 9 | 0.06 | 25 | 1 | 1 | 50 |
| 73 | TP2B | 2.354100 | 3101.00 | 1604.75000 | 21.1 | 9 | 0.06 | 25 | 1 | 1 | 50 |
| 75 | TP2C | 2.962705 | 3634.00 | 1604.75000 | 18.1 | 9 | 0.06 | 25 | 1 | 1 | 50 |
| 77 | TP3A | 5.615692 | 2792.00 | 1604.75000 | 23.2 | 12 | 0.22 | 25 | 1 | 1 | 50 |
| 79 | TP3B | 2.540119 | 4210.00 | 1604.75000 | 15.5 | 12 | 0.06 | 25 | 1 | 1 | 50 |
| 81 | TP3C | 2.842312 | 4661.00 | 1604.75000 | 13.9 | 12 | 0.06 | 25 | 1 | 1 | 50 |
Plots
(D) New calculation (substract half of the spike from A)
| Version | Author | Date |
|---|---|---|
| ced6eed | Paloma | 2025-04-03 |
Evaluation
Since all spiked samples produce negative values, we will continue our analyses using only non-spiked samples.
ggplot(dSpiked, aes(y = Final_conc_pg.mg,
x = Sample,
color = (Failed_samples))) +
# fill = factor(Spike.cont_pg.mL))) +
geom_point(size = 2.5) +
geom_text(label = c(dSpiked$Sample), nudge_y = 0.95, nudge_x = -1.2) +
geom_smooth(method = "lm",
color = "gold3",
se = TRUE,
alpha = 0.1) +
# geom_hline(yintercept = mean(data$Final_conc_pg.mg),
# color = "gray80",
# linetype = "dashed") +
theme_minimal() +
labs(
title = "Final Cort Concentration and Dilution",
y = "Final Concentration (pg/mg)",
x = "Contribution of spike (pg/ml)"
) +
theme(
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12)
) `geom_smooth()` using formula = 'y ~ x'Warning: Removed 3 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_text()`).
sessionInfo()R version 4.4.3 (2025-02-28)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.1.4 paletteer_1.6.0 broom_1.0.7 ggplot2_3.5.1
[5] knitr_1.49
loaded via a namespace (and not attached):
[1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 tidyr_1.3.1
[5] prismatic_1.1.2 stringi_1.8.4 digest_0.6.37 magrittr_2.0.3
[9] evaluate_1.0.1 grid_4.4.3 fastmap_1.2.0 rprojroot_2.0.4
[13] workflowr_1.7.1 jsonlite_1.8.9 whisker_0.4.1 backports_1.5.0
[17] rematch2_2.1.2 promises_1.3.0 purrr_1.0.2 fansi_1.0.6
[21] scales_1.3.0 jquerylib_0.1.4 cli_3.6.3 rlang_1.1.4
[25] munsell_0.5.1 withr_3.0.2 cachem_1.1.0 yaml_2.3.10
[29] tools_4.4.3 colorspace_2.1-1 httpuv_1.6.15 vctrs_0.6.5
[33] R6_2.5.1 lifecycle_1.0.4 git2r_0.35.0 stringr_1.5.1
[37] fs_1.6.5 pkgconfig_2.0.3 pillar_1.9.0 bslib_0.8.0
[41] later_1.3.2 gtable_0.3.6 glue_1.8.0 Rcpp_1.0.13-1
[45] xfun_0.49 tibble_3.2.1 tidyselect_1.2.1 rstudioapi_0.17.1
[49] farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.29 labeling_0.4.3
[53] compiler_4.4.3