The property BuildMethod specifies the method that is used to build a complete population curve from a finite set of age-specific population numbers. The table below gives the BuildMethods currently supported
| BuildMethod | Description |
|---|---|
| CONSTANT_DENSITY | Between age nodes the population density is constant |
| QUADRATIC_DENSITY | Between age nodes the population denstiy is a quadratic function of age with curvature continuous across age nodes. |
| HYBRID_DENSITY | Between age nodes the population density is a quadratic function of age. Density does not vary as much as for QUADRATIC_DENSITY but curvature is discontinuous across nodes. |
| HYBRID_EXPONENTIAL | Between age nodes the logarithm of the population denstiy is a quadratic function of age with discontinuous curvature. |
| FEENEY | Feeney's method for smoothing a population subject to age-heaping. |
| SPRAGUE | Smooth by aggregating the population into five-year groups then subdividing using Sprague. |
| WEIGHTED_AVERAGE | Smooth by a weighted moving average over age |
| CARRIER_FARRAG | Smooth by aggregating the population into five-year groups then adjusting using Carrier-Farrag then subdividing using Sprague. |
| KARUP_KING_NEWTON | Aggregate the population into five-year age groups, smooth using Karup-King-Newton then subdivide using Karup-King. |
| ARRIAGA | Aggregate the population into five-year age groups, smooth using Arriaga's light method, then subdivide using Sprague. |
| MOD_ARRIAGA | Same as Arriaga but smoothing starts at age five instead of age zero. |
| UNITED_NATIONS | Aggregate the population into five-year age groups, smooth using the United Nations method, then subdivide using Sprague. |
| STRONG | Aggregate the population into ten-year age groups, smooth using Arriaga's strong method, subdivide into five-year age groups using Arriaga's light method, then subdivide into single year age groups using Sprague. |
| PCLM | Expand an abridged population using the Penalised Composite Link Model |
| PTOPALS | Expand an abridged population using the PTOPALS (Dyrting, Flaxman, and Sharygin, 2022) |