Package {enrollcast}

A data frame or data-frame subclass with columns grade_from, grade_to, and ratio, as returned by progression_ratios(). grade_from and grade_to must not be missing. ratio must be numeric, non-negative, and finite; an infinite ratio (from a zero-enrollment feeder) is rejected, while NA/NaN ratios (e.g. from sparse history) are kept in the matrix with a warning.

Optional character vector giving the low-to-high grade order. If omitted, the order is reconstructed from the transition chain. Every non-entry grade in grade_order must appear as a grade_to in ratios. Must not contain duplicates or missing values.

A long data frame or data-frame subclass of historical enrollment with one row per grade per year. Enrollment may be NA, but non-missing values must be finite and non-negative; NaN and infinite values are rejected. Year values must be coercible to finite integers and must not be missing.

Distinct, non-missing character scalars naming columns in data. Defaults are "year", "grade", and "enrollment".

How to summarise per-year ratios into one ratio per grade: "mean" (default), "geometric", "median", "last" (most recent transition only), or "weighted".

Optional. Use only the most recent n_years available adjacent-year transitions. If n_years exceeds the number of available transitions, all are used.

For method = "weighted", a finite, non-missing, non-negative numeric vector aligned most-recent to oldest, with one weight per transition year used and a positive sum. Do not supply weights for other methods.

Optional character vector giving the low-to-high grade order. If omitted, factor levels, numeric ordering, or (with a warning) alphabetical ordering is used.

Most recent observed enrollment: either a data frame with columns grade and enrollment (optionally year), or a named numeric vector. Grade values or vector names must be present and unique. Enrollment must be finite, non-missing, and non-negative.

A data frame or data-frame subclass with columns grade_from, grade_to, and ratio, as returned by progression_ratios(). grade_from and grade_to must not be missing. ratio must be numeric, non-negative, and finite; an infinite ratio (from a zero-enrollment feeder) is rejected, while NA/NaN ratios (e.g. from sparse history) are kept in the matrix with a warning.

Number of years to project (a positive integer).

Exogenous entry-grade enrollment for each projected year: a numeric vector of length horizon, or a data frame with an enrollment or value column. Values must be finite, non-missing, and non-negative. If NULL, the entry grade is held constant at its base value and a warning is issued.

Optional prebuilt projection schedule: a list of per-year steps, each ⁠list(matrix = <square projection matrix>, entry = <NULL or a single number>)⁠, as produced by swing_schedule(). When supplied, ratios and entry must be NULL and horizon defaults to the schedule length. Each matrix must be numeric and square; non-missing coefficients must be finite and non-negative. NA/NaN coefficients are preserved and trigger a warning. A missing coefficient can make its output row missing. If that missing enrollment remains after entry replacement, the next matrix multiplication spreads missingness to all grade results because zero times a missing value is still missing. A non-NULL entry value then restores only the entry grade. Matrix row and column names must be unique and identical in the same order; all steps must use the same names. A step's entry must be NULL or one finite, non-negative number.

Optional integer label for the base year; output years run from start_year + 1. An explicit value and all resulting years must be within the R integer range. If NULL, the year is derived from base$year when present; that column must contain one unambiguous integer within the same range. With no year column, output years are ⁠1..horizon⁠.

A data frame or data-frame subclass with columns grade_from, grade_to, and ratio, as returned by progression_ratios(). grade_from and grade_to must not be missing. ratio must be numeric, non-negative, and finite; an infinite ratio (from a zero-enrollment feeder) is rejected, while NA/NaN ratios (e.g. from sparse history) are kept in the matrix with a warning.

Number of years to project (a positive integer).

Number of leading years the school is swinging (a non-negative integer); enrollment is held flat at base.

Recovery multipliers applied for one year each, immediately after the swing and compounding on the prior year: a numeric vector (whole-school, one multiplier per recovery year) or a grade-by-year numeric matrix (one row per grade). Values must be finite, non-missing, and non-negative. Named matrix rows are matched and reordered by grade; unnamed rows are interpreted in projection grade order. Use numeric(0) for no recovery window.

Exogenous entry-grade enrollment for the normal grade progression ratio years only: one finite, non-missing, non-negative numeric value for each of the horizon - swing_years - length(recovery) years after recovery. Must be empty when there are no normal years.

Optional character vector giving the low-to-high grade order. If omitted, the order is reconstructed from the transition chain. Every non-entry grade in grade_order must appear as a grade_to in ratios. Must not contain duplicates or missing values.