# Celeus - reproduce.R  (run from this package folder; bundled data in data/)
# Test: Two-way ANOVA (factorial)  |  engine 0.41.7  |  catalog c40-dc08
# Research context: not provided for this run.
# Reproduces the recorded result deterministically.

set.seed(20261101)
.data_path <- "data/factorial-design.csv"
if (!file.exists(.data_path)) stop("Run reproduce.R from the package folder (the bundled data is in data/).")
data <- readr::read_csv(.data_path, show_col_types = FALSE)  # needs the readr package
clean_cells <- function (df) 
{
    ctrl_collapse <- "[\r\n\t\v\f]+"
    zero_width_chars <- intToUtf8(c(8203L, 65279L), multiple = TRUE)
    uni_space_chars <- intToUtf8(c(160L, 8239L, 8199L), multiple = TRUE)
    clean_text <- function(x) {
        out <- x
        for (ch in zero_width_chars) out <- gsub(ch, "", out, 
            fixed = TRUE)
        out <- gsub(ctrl_collapse, " ", out)
        for (ch in uni_space_chars) out <- gsub(ch, " ", out, 
            fixed = TRUE)
        trimws(gsub(" +", " ", out))
    }
    changed_cols <- character(0)
    cells_cleaned <- 0L
    retyped <- character(0)
    for (j in seq_along(df)) {
        col <- df[[j]]
        if (!is.character(col)) 
            next
        cleaned <- clean_text(col)
        n_changed <- sum(!is.na(col) & !is.na(cleaned) & col != 
            cleaned, na.rm = TRUE)
        if (n_changed > 0L) {
            changed_cols <- c(changed_cols, names(df)[j])
            cells_cleaned <- cells_cleaned + n_changed
        }
        df[[j]] <- cleaned
        non_blank <- cleaned[!is.na(cleaned) & nzchar(cleaned)]
        if (length(non_blank) && all(grepl("^[+-]?(0|[1-9][0-9]*)([.][0-9]+)?$", 
            non_blank))) {
            parsed <- suppressWarnings(as.numeric(cleaned))
            was_blank <- is.na(cleaned) | !nzchar(cleaned)
            lost <- (is.na(parsed) | is.infinite(parsed)) & !was_blank
            if (!any(lost)) {
                finite <- parsed[!is.na(parsed)]
                all_whole <- all(finite == floor(finite))
                in_range <- all(abs(finite) <= .Machine$integer.max)
                df[[j]] <- if (all_whole && in_range) 
                  as.integer(parsed)
                else parsed
                retyped <- c(retyped, names(df)[j])
            }
        }
    }
    names(df) <- clean_text(names(df))
    if (length(changed_cols)) {
        attr(df, "ingest_clean_columns") <- changed_cols
        attr(df, "ingest_clean_cells_cleaned") <- cells_cleaned
    }
    if (length(retyped)) 
        attr(df, "ingest_clean_retyped") <- retyped
    df
}
data <- clean_cells(data)
# Complete-case analysis: drop rows missing any analysis value.
.acols <- c("len", "supp", "dose")
data <- data[stats::complete.cases(data[, .acols, drop = FALSE]), , drop = FALSE]
result <- { .d <- data; .d[["supp"]] <- as.factor(.d[["supp"]]); .d[["dose"]] <- as.factor(.d[["dose"]]); .fit <- stats::lm(`len` ~ `supp` * `dose`, data = .d); .an <- car::Anova(.fit, type = 2); .em <- emmeans::emmeans(.fit, specs = c("supp", "dose")); list(anova = .an, emmeans = summary(.em)) }
print(result)

# Effect size (partial_eta_sq) and its confidence interval.
effect <- { .es_d <- data; .es_d[["supp"]] <- as.factor(.es_d[["supp"]]); .es_d[["dose"]] <- as.factor(.es_d[["dose"]]); .es_an <- car::Anova(stats::lm(`len` ~ `supp` * `dose`, data = .es_d), type = 2); effectsize::F_to_eta2(.es_an[["F value"]][seq_len(nrow(.es_an) - 1L)], .es_an[["Df"]][seq_len(nrow(.es_an) - 1L)], .es_an[["Df"]][nrow(.es_an)], ci = 0.95, alternative = "two.sided") }
print(effect)