# Celeus - reproduce.R  (run from this package folder; bundled data in data/)
# Test: Linear mixed-effects model (random intercept and slope)  |  engine 0.53.2  |  catalog c40-dc08
# Research context: not provided for this run.
# Reproduces the recorded result deterministically.

set.seed(20261110)
.data_path <- "data/long-vs-wide-data.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("weight", "Time", "Chick")
data <- data[stats::complete.cases(data[, .acols, drop = FALSE]), , drop = FALSE]
result <- { .full <- lme4::lmer(`weight` ~ `Time` + (1 + `Time` | `Chick`), data = data, REML = TRUE); .tt <- { .X <- lme4::getME(.full, "X"); .asg <- attr(.X, "assign"); .I <- diag(ncol(.X)); t(vapply(sort(unique(.asg[.asg > 0L])), function(j) unlist(pbkrtest::KRmodcomp(.full, .I[.asg == j, , drop = FALSE])$test["Ftest", c("stat", "ndf", "ddf", "p.value")]), numeric(4L))) }; .p <- .tt[1L, "p.value"]; .ct <- { .V <- pbkrtest::vcovAdj(.full); .V0 <- as.matrix(stats::vcov(.full)); .L <- diag(ncol(.V0)); .b <- lme4::fixef(.full); .se <- sqrt(diag(as.matrix(.V))); .df <- vapply(seq_len(ncol(.V0)), function(i) pbkrtest::Lb_ddf(L = .L[i, , drop = FALSE], V0 = .V0, Vadj = .V), numeric(1L)); .tv <- .b / .se; .q <- stats::qt(1 - (1 - 0.95) / 2, .df); cbind(Estimate = .b, `Std. Error` = .se, df = .df, `t value` = .tv, `Pr(>|t|)` = 2 * stats::pt(abs(.tv), .df, lower.tail = FALSE), conf.low = .b - .q * .se, conf.high = .b + .q * .se) }; list(model = summary(.full), p_value = unname(.p), estimate = unname(.ct[2L, "Estimate"]), conf_int = unname(.ct[2L, c("conf.low", "conf.high")]), coefficients = .ct, term_tests = .tt) }
print(result)