COVID-19 Exposure & Adolescent Mental Health — Final Report
Artur Luz Nunes Queiroz
2026-02-11 17:37
- 1 1) Import & clean
- 2 2) Missing values →
NA(numeric codes) - 3 3) Symptoms: detect, enforce {0,1}, and index
- 4 4) Codebook labels (safe;
out-of-range →
NA) - 5 5) Descriptives & Table 1
- 6 6) Crosstabs (chi-square + Cramér’s V)
- 7 7) Logistic regression (moderation; centered covariates)
- 8 8) Collinearity & performance
- 9 9) Marginal effects & interaction plot
- 10 10) Publication-ready model table (optional)
- 11 11) Bias-reduced alternatives (use if separation warnings appear)
- 12 12) Export cleaned data
- 13 Appendix: Session info
1 1) Import & clean
# Location & filename stem (extension auto-detected)
data_dir <- "C:/Users/Artur/OneDrive - Florida State University/FIRST/Papers/Covid-Corbinian/Data"
file_stem <- "Covid_Yout_Data-3"
# Find a file with this stem (xlsx/xls/csv)
hits <- list.files(path = data_dir,
pattern = paste0("^", file_stem, "\\.(xlsx|xls|csv)$"),
full.names = TRUE, ignore.case = TRUE)
if (!length(hits)) {
cat("No file with stem:", file_stem, "in:\n", data_dir, "\nFiles here:\n")
print(list.files(data_dir, full.names = TRUE))
stop("Confirm the exact filename (including extension).")
}
data_path <- hits[1]
cat("Reading:", data_path, "\n")## Reading: C:/Users/Artur/OneDrive - Florida State University/FIRST/Papers/Covid-Corbinian/Data/Covid_Yout_Data-3.xlsx# Read Excel or CSV
df_raw <- if (grepl("\\.xlsx?$", data_path, ignore.case = TRUE)) {
readxl::read_excel(data_path)
} else {
readr::read_csv(data_path, show_col_types = FALSE)
}
# The Excel had trailing spaces in some headers (symptoms); trim, then clean
names(df_raw) <- trimws(names(df_raw))
df <- df_raw %>% janitor::clean_names()
cat("Rows:", nrow(df), " | Cols:", ncol(df), "\n")## Rows: 1725 | Cols: 662 2) Missing values →
NA (numeric codes)
# Dataset uses explicit missing codes (e.g., 999; sometimes 990/9999)
special_missing <- c(990, 999, 9999)
df <- df %>% mutate(across(where(is.numeric), ~ ifelse(. %in% special_missing, NA_real_, .)))3 3) Symptoms: detect, enforce {0,1}, and index
# Robust detection after clean_names() to catch variants
symptom_patterns <- c(
"^s_?headache$", "^s_?fever$", "^s_?cough$",
"^s_?sore[_]?thro$", "^s_?shor[_]?bre$", "^s_?weak$", "^s_?cold$"
)
cand <- names(df)
symptom_cols <- unique(unlist(lapply(symptom_patterns, function(pat) {
cand[stringr::str_detect(cand, stringr::regex(pat, ignore_case = TRUE))]
})))
symptom_cols <- union(symptom_cols, intersect(
names(df), c("sheadache","s_fever","s_cough","s_sore_thro","s_shor_bre","s_weak","s_cold")
))
symptom_cols <- unique(symptom_cols)
cat("Detected symptom columns:\n"); print(symptom_cols)## Detected symptom columns:## [1] "sheadache" "s_fever" "s_cough" "s_sore_thro" "s_shor_bre"
## [6] "s_weak" "s_cold"# Enforce domain {0,1}; anything else -> NA (e.g., 2, 990, 999, 9990, 9999)
if (length(symptom_cols)) {
df <- df %>%
mutate(across(all_of(symptom_cols), ~ {
x <- suppressWarnings(as.numeric(.))
ifelse(x %in% c(0,1), x, NA_real_)
}))
}
# Build index: sum of 7 binary symptoms (range 0..7)
df <- df %>% mutate(symptom_index = if (length(symptom_cols)) rowSums(across(all_of(symptom_cols)), na.rm = TRUE) else NA_real_)
summary(df$symptom_index)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 3.000 2.892 5.000 7.0004 4) Codebook labels
(safe; out-of-range → NA)
recode_factor_safe <- function(x, levels, labels, varname = "") {
x_num <- suppressWarnings(as.numeric(x))
bad <- !is.na(x_num) & !(x_num %in% levels)
if (any(bad, na.rm = TRUE)) message(sprintf("Note: %s had %d out-of-codebook value(s) -> NA.", varname, sum(bad, na.rm = TRUE)))
x_num[bad] <- NA_real_
factor(x_num, levels = levels, labels = labels)
}
if ("sex" %in% names(df)) df <- df %>% mutate(sex = recode_factor_safe(sex, c(0,1,2), c("Male","Female","Diverse"), "sex"))
if ("country" %in% names(df)) df <- df %>% mutate(country = recode_factor_safe(country, c(1,2,3), c("Germany","China","Ukraine"), "country"))
if ("native" %in% names(df)) df <- df %>% mutate(native = recode_factor_safe(native, c(1,2,3,4), c("German","Polish","Ukrainian","Spanish"), "native"))
if ("religion" %in% names(df)) df <- df %>% mutate(religion = recode_factor_safe(religion, c(1,2,3,4,5,6),
c("Protestant","Muslim","Other","Catholic","Jewish","No religion"), "religion"))
if ("religiousness" %in% names(df)) df <- df %>% mutate(religiousness = recode_factor_safe(religiousness, c(0,1),
c("Not religious","Religious"), "religiousness"))
if ("occupation" %in% names(df)) df <- df %>% mutate(occupation = recode_factor_safe(occupation, c(1,2,3,4),
c("Gymnasium","Realschule","Middle school","FOS/BOS"), "occupation"))
if ("sexual_o" %in% names(df)) df <- df %>% mutate(sexual_o = recode_factor_safe(sexual_o, c(1,2,3,4,5,6),
c("Heterosexual","Gay","Bisexual","Uncertain","Other","Prefer not to answer"), "sexual_o"))
if ("infection" %in% names(df)) df <- df %>% mutate(infection = recode_factor_safe(infection, c(0,1), c("No","Yes"), "infection"))
if ("vaccination" %in% names(df))df <- df %>% mutate(vaccination = recode_factor_safe(vaccination, c(0,1), c("No","Yes"), "vaccination"))
if ("mental" %in% names(df)) df <- df %>% mutate(mental = recode_factor_safe(mental, c(0,1), c("No","Yes"), "mental"))5 5) Descriptives & Table 1
df %>% select(any_of(c("infection","vaccination","mental","symptom_index"))) %>% summary()## infection vaccination mental symptom_index
## No : 418 No : 254 No :1157 Min. :0.000
## Yes :1302 Yes :1445 Yes : 491 1st Qu.:0.000
## NA's: 5 NA's: 26 NA's: 77 Median :3.000
## Mean :2.892
## 3rd Qu.:5.000
## Max. :7.000if (have_gtsummary) {
library(gtsummary)
df %>%
select(mental, infection, vaccination, age, sex, sexual_o, symptom_index) %>%
tbl_summary(by = mental, missing_text = "(missing)") %>%
add_p() %>%
modify_caption("**Table 1. Sample characteristics by mental health status**")
}| Characteristic | No N = 1,1571 |
Yes N = 4911 |
p-value2 |
|---|---|---|---|
| infection | 925 (80%) | 370 (76%) | 0.038 |
| (missing) | 2 | 1 | |
| vaccination | 964 (84%) | 411 (85%) | 0.8 |
| (missing) | 16 | 7 | |
| age | 0.6 | ||
| 13 | 1 (<0.1%) | 1 (0.2%) | |
| 14 | 6 (0.5%) | 1 (0.2%) | |
| 15 | 434 (38%) | 191 (39%) | |
| 16 | 592 (51%) | 257 (52%) | |
| 17 | 104 (9.0%) | 33 (6.7%) | |
| 18 | 20 (1.7%) | 7 (1.4%) | |
| (missing) | 0 | 1 | |
| sex | <0.001 | ||
| Male | 615 (53%) | 126 (26%) | |
| Female | 526 (45%) | 347 (71%) | |
| Diverse | 16 (1.4%) | 16 (3.3%) | |
| (missing) | 0 | 2 | |
| sexual_o | <0.001 | ||
| Heterosexual | 948 (83%) | 303 (63%) | |
| Gay | 7 (0.6%) | 15 (3.1%) | |
| Bisexual | 52 (4.6%) | 60 (13%) | |
| Uncertain | 67 (5.9%) | 64 (13%) | |
| Other | 11 (1.0%) | 17 (3.5%) | |
| Prefer not to answer | 54 (4.7%) | 20 (4.2%) | |
| (missing) | 18 | 12 | |
| symptom_index | <0.001 | ||
| 0 | 255 (22%) | 128 (26%) | |
| 1 | 96 (8.3%) | 25 (5.1%) | |
| 2 | 129 (11%) | 33 (6.7%) | |
| 3 | 190 (16%) | 45 (9.2%) | |
| 4 | 167 (14%) | 75 (15%) | |
| 5 | 164 (14%) | 80 (16%) | |
| 6 | 129 (11%) | 73 (15%) | |
| 7 | 27 (2.3%) | 32 (6.5%) | |
| 1 n (%) | |||
| 2 Pearson’s Chi-squared test; Fisher’s exact test | |||
6 6) Crosstabs (chi-square + Cramér’s V)
do_chisq <- function(a, b) {
tb <- table(a, b, useNA = "no")
print(tb)
chi <- suppressWarnings(chisq.test(tb))
print(chi)
if (have_vcd) {
vcd::assocstats(tb) # includes Cramér's V
} else {
n <- sum(tb); phi2 <- as.numeric(chi$statistic) / n; k <- min(nrow(tb), ncol(tb))
cramer_v <- sqrt(phi2 / (k - 1))
cat("Cramer's V (manual):", round(cramer_v, 3), "\n")
}
}
if (all(c("infection","mental") %in% names(df))) {
cat("\n### Infection × Mental\n"); do_chisq(df$infection, df$mental)
}##
## ### Infection × Mental
## b
## a No Yes
## No 230 120
## Yes 925 370
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tb
## X-squared = 4.0329, df = 1, p-value = 0.04462## X^2 df P(> X^2)
## Likelihood Ratio 4.2200 1 0.039950
## Pearson 4.3018 1 0.038072
##
## Phi-Coefficient : 0.051
## Contingency Coeff.: 0.051
## Cramer's V : 0.051if (all(c("vaccination","mental") %in% names(df))) {
cat("\n### Vaccination × Mental\n"); do_chisq(df$vaccination, df$mental)
}##
## ### Vaccination × Mental
## b
## a No Yes
## No 177 73
## Yes 964 411
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tb
## X-squared = 0.020899, df = 1, p-value = 0.8851## X^2 df P(> X^2)
## Likelihood Ratio 0.048435 1 0.82581
## Pearson 0.048284 1 0.82608
##
## Phi-Coefficient : 0.005
## Contingency Coeff.: 0.005
## Cramer's V : 0.0057 7) Logistic regression (moderation; centered covariates)
# Center continuous covariates
df <- df %>% mutate(
age_c = age - mean(age, na.rm = TRUE),
symptom_index_c = symptom_index - mean(symptom_index, na.rm = TRUE)
)
# Build complete-case modeling frame
model_vars <- c("mental","infection","vaccination","age_c","sex","sexual_o","symptom_index_c")
df_model <- df %>%
select(all_of(model_vars)) %>%
drop_na() %>%
mutate(across(where(is.factor), fct_drop))
# Defensive: keep interaction only if both predictors have ≥2 levels
need_2_levels <- function(x) !is.factor(x) || nlevels(x) >= 2
has_infection_ok <- "infection" %in% names(df_model) && need_2_levels(df_model$infection)
has_vaccination_ok <- "vaccination" %in% names(df_model) && need_2_levels(df_model$vaccination)
covars <- c("age_c","sex","sexual_o","symptom_index_c")
keep_covars <- covars[covars %in% names(df_model)]
keep_covars <- keep_covars[sapply(df_model[keep_covars], need_2_levels)]
rhs <- if (has_infection_ok && has_vaccination_ok) {
c("infection * vaccination", keep_covars)
} else if (has_vaccination_ok) {
message("NOTE: 'infection' has < 2 levels after filtering; modeling without it.")
c("vaccination", keep_covars)
} else if (has_infection_ok) {
message("NOTE: 'vaccination' has < 2 levels after filtering; modeling without it.")
c("infection", keep_covars)
} else stop("Neither infection nor vaccination retained ≥ 2 levels after filtering.")
form <- as.formula(paste("mental ~", paste(rhs, collapse = " + ")))
form## mental ~ infection * vaccination + age_c + sex + sexual_o + symptom_index_cfit <- glm(form, data = df_model, family = binomial, na.action = na.exclude)
summary(fit)##
## Call:
## glm(formula = form, family = binomial, data = df_model, na.action = na.exclude)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.06734 0.37792 -2.824 0.004739 **
## infectionYes -0.81105 0.41411 -1.959 0.050166 .
## vaccinationYes 0.11521 0.37355 0.308 0.757770
## age_c 0.07005 0.08594 0.815 0.415015
## sexFemale 1.04671 0.13200 7.930 2.20e-15 ***
## sexDiverse 0.78815 0.45037 1.750 0.080113 .
## sexual_oGay 1.98622 0.51042 3.891 9.97e-05 ***
## sexual_oBisexual 0.91553 0.21051 4.349 1.37e-05 ***
## sexual_oUncertain 0.83067 0.19746 4.207 2.59e-05 ***
## sexual_oOther 1.58975 0.45049 3.529 0.000417 ***
## sexual_oPrefer not to answer -0.23598 0.29464 -0.801 0.423187
## symptom_index_c 0.18314 0.03925 4.666 3.07e-06 ***
## infectionYes:vaccinationYes -0.23903 0.41418 -0.577 0.563856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1926.4 on 1588 degrees of freedom
## Residual deviance: 1728.5 on 1576 degrees of freedom
## AIC: 1754.5
##
## Number of Fisher Scoring iterations: 4broom::tidy(fit, conf.int = TRUE, exponentiate = TRUE) %>% arrange(p.value)## # A tibble: 13 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 sexFemale 2.85 0.132 7.93 2.20e-15 2.20 3.70
## 2 symptom_index_c 1.20 0.0392 4.67 3.07e- 6 1.11 1.30
## 3 sexual_oBisexual 2.50 0.211 4.35 1.37e- 5 1.65 3.78
## 4 sexual_oUncertain 2.29 0.197 4.21 2.59e- 5 1.56 3.38
## 5 sexual_oGay 7.29 0.510 3.89 9.97e- 5 2.80 21.4
## 6 sexual_oOther 4.90 0.450 3.53 4.17e- 4 2.05 12.2
## 7 (Intercept) 0.344 0.378 -2.82 4.74e- 3 0.159 0.707
## 8 infectionYes 0.444 0.414 -1.96 5.02e- 2 0.200 1.02
## 9 sexDiverse 2.20 0.450 1.75 8.01e- 2 0.895 5.30
## 10 age_c 1.07 0.0859 0.815 4.15e- 1 0.906 1.27
## 11 sexual_oPrefer not … 0.790 0.295 -0.801 4.23e- 1 0.432 1.38
## 12 infectionYes:vaccin… 0.787 0.414 -0.577 5.64e- 1 0.342 1.75
## 13 vaccinationYes 1.12 0.374 0.308 7.58e- 1 0.549 2.408 8) Collinearity & performance
# Term-level VIFs (use GVIF^(1/(2*Df)) for factors/interactions)
vif_tbl <- tryCatch({
v <- car::vif(fit)
if (is.vector(v)) {
tibble(term = names(v), df = 1L, index = as.numeric(v), type = "VIF")
} else {
tibble(term = rownames(v),
df = if ("Df" %in% colnames(v)) v[, "Df"] else 1L,
gvif = if ("GVIF" %in% colnames(v)) v[, "GVIF"] else as.numeric(v)) |>
mutate(index = if_else(df > 1, gvif^(1/(2*df)), gvif),
type = if_else(df > 1, "GVIF^(1/(2*Df))", "VIF")) |>
select(term, df, type, index) |>
arrange(desc(index))
}
}, error = function(e) NULL)
if (!is.null(vif_tbl)) {
cat("\n### Collinearity indices (term-level)\n"); print(vif_tbl, n = nrow(vif_tbl))
}##
## ### Collinearity indices (term-level)
## # A tibble: 7 × 4
## term df type index
## <chr> <dbl> <chr> <dbl>
## 1 infection:vaccination 1 VIF 11.4
## 2 infection 1 VIF 8.68
## 3 vaccination 1 VIF 5.34
## 4 symptom_index_c 1 VIF 2.33
## 5 sex 2 GVIF^(1/(2*Df)) 1.08
## 6 age_c 1 VIF 1.03
## 7 sexual_o 5 GVIF^(1/(2*Df)) 1.03# GLM-friendly collinearity summary
if (have_perf) {
library(performance)
cat("\n### performance::check_collinearity\n")
print(performance::check_collinearity(fit))
}##
## ### performance::check_collinearity## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI adj. VIF Tolerance Tolerance 95% CI
## age_c 1.03 [ 1.01, 1.16] 1.02 0.97 [0.86, 0.99]
## sex 1.35 [ 1.28, 1.45] 1.16 0.74 [0.69, 0.78]
## sexual_o 1.33 [ 1.26, 1.42] 1.15 0.75 [0.70, 0.79]
## symptom_index_c 2.33 [ 2.16, 2.52] 1.52 0.43 [0.40, 0.46]
##
## Moderate Correlation
##
## Term VIF VIF 95% CI adj. VIF Tolerance Tolerance 95% CI
## infection 8.68 [ 7.92, 9.52] 2.95 0.12 [0.11, 0.13]
## vaccination 5.34 [ 4.90, 5.84] 2.31 0.19 [0.17, 0.20]
##
## High Correlation
##
## Term VIF VIF 95% CI adj. VIF Tolerance Tolerance 95% CI
## infection:vaccination 11.36 [10.35, 12.47] 3.37 0.09 [0.08, 0.10]# Pseudo-R2
if (have_pscl) {
library(pscl)
cat("\n### Pseudo-R2 (pscl::pR2)\n")
print(pscl::pR2(fit))
}##
## ### Pseudo-R2 (pscl::pR2)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -864.2389989 -963.1780719 197.8781459 0.1027215 0.1170882 0.1666760# ROC / AUC
if (have_pROC) {
library(pROC)
roc_obj <- roc(df_model$mental, fitted(fit))
cat("\n### ROC AUC:\n"); print(auc(roc_obj))
plot(roc_obj, col = "#2C7BB6", lwd = 2, main = "ROC curve (logit model)")
}##
## ### ROC AUC:
## Area under the curve: 0.7147
# Hosmer–Lemeshow
if (have_RS) {
library(ResourceSelection)
cat("\n### Hosmer–Lemeshow test (g=10)\n")
print(hoslem.test(as.integer(df_model$mental) - 1, fitted(fit), g = 10))
}##
## ### Hosmer–Lemeshow test (g=10)
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: as.integer(df_model$mental) - 1, fitted(fit)
## X-squared = 2.7318, df = 8, p-value = 0.959 9) Marginal effects & interaction plot
# Average marginal effects for infection & vaccination
if (have_margins) {
library(margins)
cat("\n### Average marginal effects (infection, vaccination)\n")
print(summary(margins(fit, variables = c("infection","vaccination"))))
}##
## ### Average marginal effects (infection, vaccination)
## factor AME SE z p lower upper
## infectionYes -0.1963 0.0414 -4.7401 0.0000 -0.2775 -0.1152
## vaccinationYes -0.0130 0.0302 -0.4311 0.6664 -0.0721 0.0461# Predicted probabilities by Infection × Vaccination (if interaction present)
if (have_ggeffects) {
library(ggeffects); library(ggplot2)
if (all(c("infection","vaccination") %in% all.vars(form))) {
eff <- ggpredict(fit, terms = c("infection","vaccination"))
ggplot(eff, aes(x = x, y = predicted, colour = group)) +
geom_line(linewidth = 1.2) + geom_point(size = 2) +
labs(x = "Infection", y = "Pr(Mental = Yes)", colour = "Vaccination",
title = "Predicted probability by Infection × Vaccination") +
theme_minimal(base_size = 12)
} else {
message("Interaction not in the model — plot skipped.")
}
}
10 10) Publication-ready model table (optional)
if (have_modelsum) {
library(modelsummary)
modelsummary(list("Mental health (logit)" = fit),
exponentiate = TRUE,
estimate = "{estimate} ({conf.low}, {conf.high})",
gof_omit = "AIC|BIC|Log.Lik|Deviance",
output = "markdown")
}| Mental health (logit) | |
|---|---|
| (Intercept) | 0.344 (0.159, 0.707) |
| (0.130) | |
| infectionYes | 0.444 (0.200, 1.021) |
| (0.184) | |
| vaccinationYes | 1.122 (0.549, 2.397) |
| (0.419) | |
| age_c | 1.073 (0.906, 1.269) |
| (0.092) | |
| sexFemale | 2.848 (2.204, 3.699) |
| (0.376) | |
| sexDiverse | 2.199 (0.895, 5.297) |
| (0.991) | |
| sexual_oGay | 7.288 (2.800, 21.409) |
| (3.720) | |
| sexual_oBisexual | 2.498 (1.654, 3.782) |
| (0.526) | |
| sexual_oUncertain | 2.295 (1.557, 3.380) |
| (0.453) | |
| sexual_oOther | 4.903 (2.047, 12.159) |
| (2.209) | |
| sexual_oPrefer not to answer | 0.790 (0.432, 1.380) |
| (0.233) | |
| symptom_index_c | 1.201 (1.113, 1.298) |
| (0.047) | |
| infectionYes × vaccinationYes | 0.787 (0.342, 1.750) |
| (0.326) | |
| Num.Obs. | 1589 |
| F | 14.027 |
| RMSE | 0.43 |
11 11) Bias-reduced alternatives (use if separation warnings appear)
if (have_logistf) {
library(logistf)
fit_firth <- logistf(form, data = df_model)
summary(fit_firth)
exp(cbind(OR = coef(fit_firth), confint(fit_firth)))
}
if (have_brglm2) {
library(brglm2)
fit_br <- glm(form, data = df_model, family = binomial, method = brglmFit, type = "AS_median")
summary(fit_br)
broom::tidy(fit_br, conf.int = TRUE, exponentiate = TRUE)
}12 12) Export cleaned data
clean_path <- file.path(dirname(data_path), "Covid_Youth_Data_cleaned.csv")
readr::write_csv(df, clean_path)
cat("Saved cleaned data to:\n", clean_path, "\n")## Saved cleaned data to:
## C:/Users/Artur/OneDrive - Florida State University/FIRST/Papers/Covid-Corbinian/Data/Covid_Youth_Data_cleaned.csv13 Appendix: Session info
sessionInfo()## R version 4.4.1 (2024-06-14 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] modelsummary_2.5.0 ggeffects_2.3.2 margins_0.3.28
## [4] ResourceSelection_0.3-6 pROC_1.19.0.1 pscl_1.5.9
## [7] performance_0.16.0 gtsummary_2.5.0 car_3.1-5
## [10] carData_3.0-6 broom_1.0.12 janitor_2.2.1
## [13] readxl_1.4.5 lubridate_1.9.5 forcats_1.0.1
## [16] stringr_1.6.0 dplyr_1.2.0 purrr_1.2.1
## [19] readr_2.1.6 tidyr_1.3.2 tibble_3.3.1
## [22] ggplot2_4.0.2 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] Rdpack_2.6.6 rlang_1.1.7 magrittr_2.0.4
## [4] snakecase_0.11.1 prediction_0.3.18 compiler_4.4.1
## [7] mgcv_1.9-4 brglm2_1.0.1 vctrs_0.7.1
## [10] crayon_1.5.3 pkgconfig_2.0.3 shape_1.4.6.1
## [13] fastmap_1.2.0 backports_1.5.0 labeling_0.4.3
## [16] effectsize_1.0.1 utf8_1.2.6 rmarkdown_2.30
## [19] markdown_2.0 tzdb_0.5.0 haven_2.5.5
## [22] nloptr_2.2.1 bit_4.6.0 xfun_0.56
## [25] glmnet_4.1-10 jomo_2.7-6 logistf_1.26.1
## [28] cachem_1.1.0 litedown_0.9 jsonlite_2.0.0
## [31] tinytable_0.16.0 pan_1.9 parallel_4.4.1
## [34] R6_2.6.1 bslib_0.10.0 tables_0.9.33
## [37] stringi_1.8.7 vcd_1.4-13 RColorBrewer_1.1-3
## [40] rpart_4.1.24 boot_1.3-32 lmtest_0.9-40
## [43] jquerylib_0.1.4 cellranger_1.1.0 numDeriv_2016.8-1.1
## [46] Rcpp_1.1.1 iterators_1.0.14 knitr_1.51
## [49] zoo_1.8-15 parameters_0.28.3 Matrix_1.7-4
## [52] splines_4.4.1 nnet_7.3-20 timechange_0.4.0
## [55] tidyselect_1.2.1 rstudioapi_0.18.0 abind_1.4-8
## [58] yaml_2.3.12 codetools_0.2-20 lattice_0.22-7
## [61] enrichwith_0.4.0 bayestestR_0.17.0 withr_3.0.2
## [64] S7_0.2.1 evaluate_1.0.5 survival_3.8-6
## [67] xml2_1.5.2 pillar_1.11.1 mice_3.19.0
## [70] checkmate_2.3.4 foreach_1.5.2 reformulas_0.4.4
## [73] insight_1.4.6 generics_0.1.4 vroom_1.7.0
## [76] nleqslv_3.3.5 hms_1.1.4 commonmark_2.0.0
## [79] scales_1.4.0 minqa_1.2.8 glue_1.8.0
## [82] tools_4.4.1 data.table_1.18.2.1 lme4_1.1-38
## [85] fs_1.6.6 grid_4.4.1 rbibutils_2.4.1
## [88] datawizard_1.3.0 cards_0.7.1 colorspace_2.1-2
## [91] nlme_3.1-168 formula.tools_1.7.1 cardx_0.3.2
## [94] Formula_1.2-5 cli_3.6.5 gt_1.3.0
## [97] gtable_0.3.6 sass_0.4.10 digest_0.6.39
## [100] operator.tools_1.6.3.1 farver_2.1.2 htmltools_0.5.9
## [103] lifecycle_1.0.5 mitml_0.4-5 statmod_1.5.1
## [106] bit64_4.6.0-1 MASS_7.3-65