fastLowess Node.js API Reference

The Node.js bindings provide a high-performance interface to the core Rust library, mirroring the Rust API structure.

Classes

Lowess

The Lowess class allows configuring the LOWESS parameters once and fitting multiple datasets using those parameters.

Constructor:

const { Lowess } = require('fastlowess');

const model = new Lowess({ fraction: 0.5, iterations: 3 });

• options: An object containing LowessOptions fields.

Methods:

const { Lowess } = require('fastlowess');

const n = 100;
const x = Float64Array.from({ length: n }, (_, i) => i * 2 * Math.PI / (n - 1));
const y = Float64Array.from(x, (xi, i) => Math.sin(xi) + (((i * 7 + 3) % 17) / 17 - 0.5) * 0.6);

const model = new Lowess({ fraction: 0.5 });
const result = model.fit(x, y);

• Fits the model to the provided x and y typed arrays.
• Returns a LowessResult object containing the smoothed values and optional diagnostics.

StreamingLowess

The StreamingLowess class processes data in chunks, suitable for very large datasets or streaming applications.

Constructor:

const { StreamingLowess } = require('fastlowess');

const stream = new StreamingLowess({ fraction: 0.3 }, { chunk_size: 50, overlap: 10 });

• options: An object containing LowessOptions fields.
• streamingOptions: An object containing StreamingOptions fields.

Methods:

const { StreamingLowess } = require('fastlowess');

const n = 100;
const x = Float64Array.from({ length: n }, (_, i) => i * 2 * Math.PI / (n - 1));
const y = Float64Array.from(x, (xi, i) => Math.sin(xi) + (((i * 7 + 3) % 17) / 17 - 0.5) * 0.6);

const stream = new StreamingLowess({ fraction: 0.3 }, { chunk_size: 50, overlap: 10 });
const partialResult = stream.process_chunk(x.slice(0, 50), y.slice(0, 50));

• Processes a chunk of data. Returns partial results.

const { StreamingLowess } = require('fastlowess');

const n = 100;
const x = Float64Array.from({ length: n }, (_, i) => i * 2 * Math.PI / (n - 1));
const y = Float64Array.from(x, (xi, i) => Math.sin(xi) + (((i * 7 + 3) % 17) / 17 - 0.5) * 0.6);

const stream = new StreamingLowess({ fraction: 0.3 }, { chunk_size: 50, overlap: 10 });
stream.process_chunk(x, y);
const finalResult = stream.finalize();

• Finalizes the smoothing process and returns any remaining buffered results.

OnlineLowess

The OnlineLowess class updates the model incrementally with new data points.

Constructor:

const { OnlineLowess } = require('fastlowess');

const online = new OnlineLowess({ fraction: 0.3 }, { window_capacity: 50, min_points: 5 });

• options: An object containing LowessOptions fields.
• onlineOptions: An object containing OnlineOptions fields.

Methods:

const { OnlineLowess } = require('fastlowess');

const online = new OnlineLowess({ fraction: 0.3 }, { window_capacity: 50, min_points: 5 });
const result = online.add_point(1.0, 2.0);  // returns OnlineOutput | null

• Adds a single point to the sliding window and returns an OnlineOutput once enough points are available, or null while the window is still filling.

Options Structures

LowessOptions

Field	Type	Default	Description
fraction	number	0.67	Smoothing fraction (bandwidth)
iterations	number	3	Number of robustifying iterations
delta	number	NaN	Interpolation distance (NaN for auto)
weight_function	string	"tricube"	Weight function name
robustness_method	string	"bisquare"	Robustness method name
scaling_method	string	"mad"	Residual scaling method
boundary_policy	string	"extend"	Boundary handling policy
zero_weight_fallback	string	"use_local_mean"	Zero-weight handling
auto_converge	number	null	Auto-convergence tolerance
confidence_intervals	number	null	Confidence level (e.g., 0.95)
prediction_intervals	number	null	Prediction level (e.g., 0.95)
return_diagnostics	boolean	false	Include diagnostics in result
return_residuals	boolean	false	Include residuals in result
return_robustness_weights	boolean	false	Include weights in result
return_se	boolean	false	Return standard errors
parallel	boolean	true	Enable parallel execution
cv_method	string	"kfold"	CV method ("kfold" or "loocv") (Batch only)
cv_k	number	5	Number of folds for k-fold CV (Batch only)
cv_fractions	number[]	null	Fractions to test for cross-validation (Batch only)
cv_seed	number	null	Random seed for cross-validation shuffling (Batch only)
custom_weights	Float64Array	null	Per-observation case weights — passed to fit(), not the options object (Batch only)

StreamingOptions (inherits LowessOptions)

Field	Type	Default	Description
chunk_size	number	5000	Data chunk size
overlap	number	500	Overlap between chunks
merge_strategy	string	"weighted_average"	Strategy for blending overlap regions

OnlineOptions (inherits LowessOptions)

Field	Type	Default	Description
window_capacity	number	1000	Max points in sliding window
min_points	number	3	Min points before smoothing starts
update_mode	string	"full"	Update mode ("full" or "incremental")
parallel	boolean	false	Enable parallel execution (off by default; online LOWESS fits one point at a time)

Result Structure

OnlineOutput

Returned by add_point() once the window has enough points (null until then).

Field	Type	Description
smoothed	number	Smoothed value for the latest point
std_error	number \| null	Standard error (if requested)
residual	number \| null	Residual y − smoothed (if requested)
robustness_weight	number \| null	Robustness weight (if requested)
iterations_used	number \| null	Robustness iterations performed

LowessResult

Field	Type	Description
x	Float64Array	Sorted x values
y	Float64Array	Smoothed y values
fraction_used	number	Fraction used (set or selected by CV)
iterations_used	number \| null	Robustness iterations actually performed
standard_errors	Float64Array \| null	Per-point standard errors
confidence_lower	Float64Array \| null	Lower confidence bounds
confidence_upper	Float64Array \| null	Upper confidence bounds
prediction_lower	Float64Array \| null	Lower prediction bounds
prediction_upper	Float64Array \| null	Upper prediction bounds
residuals	Float64Array \| null	Residuals (if return_residuals)
robustness_weights	Float64Array \| null	Robustness weights (if return_robustness_weights)
cv_scores	Float64Array \| null	CV score per tested fraction
diagnostics	Diagnostics \| null	Fit metrics (if return_diagnostics)

Diagnostics

Field	Type	Description
rmse	number	Root Mean Squared Error
mae	number	Mean Absolute Error
r_squared	number	R-squared
residual_sd	number	Residual standard deviation
effective_df	number | null	Effective degrees of freedom
aic	number | null	AIC
aicc	number | null	AICc

Options

weight_function

• "tricube" (default)
• "epanechnikov"
• "gaussian"
• "uniform" (alias: "boxcar")
• "biweight" (alias: "bisquare")
• "triangle" (alias: "triangular")
• "cosine"

robustness_method

• "bisquare" (default; alias: "biweight")
• "huber"
• "talwar"

boundary_policy

• "extend" (default; alias: "pad")
• "reflect" (alias: "mirror")
• "zero"
• "noboundary" (alias: "none")

scaling_method

• "mad" (default; alias: "median_absolute_deviation")
• "mar" (alias: "median_absolute_residual")
• "mean" (alias: "mean_absolute_residual")

zero_weight_fallback

• "use_local_mean" (default; aliases: "local_mean", "mean")
• "return_original" (alias: "original")
• "return_none" (alias: "none")

merge_strategy

• "weighted_average" (default; alias: "weighted")
• "average" (alias: "mean")
• "take_first" (alias: "first")
• "take_last" (alias: "last")

update_mode

• "full" (default; alias: "resmooth")
• "incremental" (alias: "single")

Example

const { Lowess } = require('fastlowess');

const x = new Float64Array([1, 2, 3, 4, 5]);
const y = new Float64Array([2.1, 4.0, 6.2, 8.0, 10.1]);

// Configure model
const model = new Lowess({ fraction: 0.5 });

// Fit data
const result = model.fit(x, y);

console.log("Smoothed Y:", result.y);