Index · statili

# _round function

packages/compat-regression/src/index.ts:22

      signature
      
_round(number: number, precision: number): number

Rounds to precision decimal places.

Exported because regression exports it as _round, and some callers reach for it to format their own values consistently with the fit.

Parameters

Name	Type	Description
number	number	—
precision	number	—

Returns

number

# exponential function

packages/compat-regression/src/index.ts:122

      signature
      
exponential(data: DataPoint[], options?: Partial<Options>): Result

Fits y = a·e^(bx). Returns coefficients as [a, b].

Uses the same y-weighted least squares as regression, which tracks the data in its original units more closely than plain regression on ln(y).

Parameters

Name	Type	Description
data	DataPoint[]	`[x, y]` pairs. Requires y > 0.
optionsoptional	Partial<Options> \| undefined	—

Returns

Result

# linear function

packages/compat-regression/src/index.ts:98

      signature
      
linear(data: DataPoint[], options?: Partial<Options>): Result

Fits a straight line, y = mx + c. Returns coefficients as [m, c].

Parameters

Name	Type	Description
data	DataPoint[]	`[x, y]` pairs. Rows with a `null` y are skipped.
optionsoptional	Partial<Options> \| undefined	`{ precision }`.

Returns

Result

Example

const result = linear([[0, 1], [32, 67], [12, 79]]);
result.equation;  // [gradient, intercept]
result.predict(5);

# logarithmic function

packages/compat-regression/src/index.ts:137

      signature
      
logarithmic(data: DataPoint[], options?: Partial<Options>): Result

Fits y = a + b·ln(x). Returns coefficients as [a, b].

Parameters

Name	Type	Description
data	DataPoint[]	`[x, y]` pairs. Requires x > 0.
optionsoptional	Partial<Options> \| undefined	—

Returns

Result

# polynomial function

packages/compat-regression/src/index.ts:172

      signature
      
polynomial(data: DataPoint[], options?: Partial<Options>): Result

Fits a polynomial of the given order.

Returns coefficients highest power first — [aₙ, …, a₁, a₀] — matching regression. Note this is the reverse of @statili/stats, which orders them ascending.

Parameters

Name	Type	Description
data	DataPoint[]	`[x, y]` pairs.
optionsoptional	Partial<Options> \| undefined	`{ order, precision }`.

Returns

Result

# power function

packages/compat-regression/src/index.ts:152

      signature
      
power(data: DataPoint[], options?: Partial<Options>): Result

Fits y = a·x^b. Returns coefficients as [a, b].

Parameters

Name	Type	Description
data	DataPoint[]	`[x, y]` pairs. Requires x > 0 and y > 0.
optionsoptional	Partial<Options> \| undefined	—

Returns

Result

# default const

packages/compat-regression/src/index.ts:209

      signature
      
regression = { linear, exponential, logarithmic, power, polynomial, _round }

The five fitting methods, matching the default export of regression.

Examples

Before

import regression from "regression";
// After — the only line that changes
import regression from "@statili/compat-regression";

const result = regression.linear([[0, 1], [32, 67], [12, 79]]);

What the shim adds: a way to know the fit failed

const result = regression.linear([[1, 1], [1, 2], [1, 3]]);
if (!result.ok) console.warn(result.error.message);
// `regression` answers this with a confident horizontal line and r2 = 0.