Package: rfastloess 3.0.0

rfastloess: High-Performance LOESS Smoothing for R

Provides high-performance LOESS smoothing (Locally Estimated Scatterplot Smoothing) using a 'Rust' backend. Supports various weight functions, robustness iterations, streaming/online processing, cross-validation, and uncertainty quantification. Applicable to genomic data, time series, and general-purpose smoothing tasks.

Authors:Amir Valizadeh [aut, cre]

rfastloess_3.0.0.tar.gz
rfastloess_3.0.0.zip(r-4.7-x86_64)rfastloess_3.0.0.zip(r-4.7-arm64)rfastloess_3.0.0.zip(r-4.6-x86_64)rfastloess_3.0.0.zip(r-4.6-arm64)rfastloess_3.0.0.zip(r-4.5-x86_64)
rfastloess_3.0.0.tgz(r-4.6-x86_64)rfastloess_3.0.0.tgz(r-4.6-arm64)rfastloess_3.0.0.tgz(r-4.5-x86_64)rfastloess_3.0.0.tgz(r-4.5-arm64)
rfastloess_3.0.0.tar.gz(r-4.7-arm64)rfastloess_3.0.0.tar.gz(r-4.7-x86_64)rfastloess_3.0.0.tar.gz(r-4.6-arm64)rfastloess_3.0.0.tar.gz(r-4.6-x86_64)
rfastloess_3.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rfastloess/json (API)

# Install 'rfastloess' in R:
install.packages('rfastloess', repos = c('https://thisisamirv.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/thisisamirv/loess-project/issues

Pkgdown/docs site:https://thisisamirv.github.io

On CRAN:

Conda:

rustcargo

5.96 score 3 stars 20 scripts 11 exports 0 dependencies

Last updated from:fe706fe1d9. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK132
linux-devel-x86_64OK158
source / vignettesOK248
linux-release-arm64OK126
linux-release-x86_64OK159
macos-release-arm64OK129
macos-release-x86_64OK358
macos-oldrel-arm64OK157
macos-oldrel-x86_64OK231
windows-devel-arm64OK315
windows-devel-x86_64OK225
windows-release-arm64OK291
windows-release-x86_64OK202
windows-oldrel-x86_64OK191
wasm-releaseOK157

Exports:add_pointcv_optsfinalizefitintervals_optsLoessOnlineLoesspredict_windowprocess_chunkStreamingLoesswindow_diagnostics

Dependencies:

Benchmarks
Rendered frombenchmarks.Rmdusingknitr::rmarkdown

Last update: 2026-10-06
Started: 2026-08-23

Alternative Software
Overview | Comparing fitted values | Why defaults differ | What this package adds
Rendered fromalternative-software.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-09-29

Confidence and Prediction Intervals
Overview | Confidence Intervals | Prediction Intervals | Both Intervals | Confidence Levels | Standard Errors | Availability
Rendered fromintervals.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-08-23

Custom Weights
How Custom Weights Work | When to Use Custom Weights | Custom Weights vs. Robustness Iterations | Basic Usage | Suppress a Known Outlier | Emphasize Important Points | Propagate Measurement Uncertainty | Combined with Robustness Iterations | Validation Rules | See Also
Rendered fromcustom-weights.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-08-23

Choosing an Execution Mode
Quick Decision Guide | Batch Adapter | When to Use | Example | Streaming Adapter | Parameters | Merge Strategies | Online Adapter | Update Modes | Feature Comparison | Next Steps
Rendered fromadapter-choice.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Cross-Validation
Overview | K-Fold Cross-Validation | Leave-One-Out (LOOCV) | Seeded Randomization | Comparison | CV Metrics | Interpreting Results | Availability | Best Practices
Rendered fromcross-validation.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Quick Start
Basic Smoothing | With Confidence Intervals | Handling Outliers | Streaming Mode | Online Mode | Plotting Results | Next Steps
Rendered fromquickstart.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Use Case: Genomic Data Smoothing
Overview | Methylation Profile Smoothing | The Challenge | Solution | ChIP-seq Signal Smoothing | Application | Large Genome Coverage (Streaming) | Best Practices for Genomic Data | See Also
Rendered fromuse-case-genomics.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Use Case: Time Series Analysis
Overview | Basic Trend Extraction | Seasonal Decomposition | Irregular Time Grids | Forecasting with Prediction Intervals | Handling Missing Data | Multi-Scale Analysis | Gene Expression Time Course | Choosing Fraction for Time Series | See Also
Rendered fromuse-case-time-series.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Merge Strategies
Overview | Average | Take First | Take Last | Weighted Average | Choosing a Strategy | Choosing Chunk Size and Overlap
Rendered frommerge.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Multivariate LOESS
Overview | 1D — Standard (Default) | 2D — Spatial Surface | 3D and Higher | Distance Metrics for Multivariate Data | Surface Mode for Multivariate Data
Rendered fromdimensions.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Polynomial Degree
Overview | Degree 0 — Local Constant | Degree 1 — Local Linear (Default) | Degree 2 — Local Quadratic | Degree 3 — Local Cubic | Degree 4 — Local Quartic | Degree and Boundary Fallback | Choosing the Right Degree | Higher Degree Effects | Surface Mode
Rendered fromdegree.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Robustness
How Robustness Works | Robustness Methods | Bisquare (Default) | Huber | Talwar | Comparison | Detecting Outliers | Scale Estimation | Auto-Convergence
Rendered fromrobustness.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-23

Boundary Handling
Overview | Extend (Default) | Reflect | Zero | No Boundary | Comparing Policies | Choosing a Policy
Rendered fromboundary.Rmdusingknitr::rmarkdown

Last update: 2026-09-05
Started: 2026-08-23

LOESS Concepts
What is LOESS? | How It Works | The Fraction Parameter | Robustness Iterations | Confidence vs Prediction Intervals | Execution Modes | Quick Decision Guide | Key Advantages | Next Steps
Rendered fromconcepts.Rmdusingknitr::rmarkdown

Last update: 2026-09-05
Started: 2026-08-23

Scaling Methods
Overview | MAD — Median Absolute Deviation (Default) | MAR — Median Absolute Residual | Mean — Mean Absolute Residual | Choosing a Scaling Method | Comparing Scaling Methods
Rendered fromscaling.Rmdusingknitr::rmarkdown

Last update: 2026-09-05
Started: 2026-08-23

Use Case: Real-Time Processing
Overview | Online Mode: Point-by-Point | Sensor Data Example | Accumulating Results | Streaming Mode: Chunk Processing | Log File Processing | Real-Time Dashboard Example | Choosing Parameters | Online Mode | Streaming Mode | Performance Considerations | See Also
Rendered fromuse-case-real-time.Rmdusingknitr::rmarkdown

Last update: 2026-09-05
Started: 2026-08-23

Weight Functions (Kernels)
Overview | Available Kernels | Tricube (Default) | Epanechnikov | Gaussian | Biweight | Cosine | Triangle | Uniform | Choosing a Kernel
Rendered fromkernels.Rmdusingknitr::rmarkdown

Last update: 2026-09-05
Started: 2026-08-23

Installation
From R-universe (recommended) | From conda-forge | From Source (GitHub) | System Requirements | Verifying Installation
Rendered frominstallation.Rmdusingknitr::rmarkdown

Last update: 2026-08-23
Started: 2026-08-23