rfastlowess: High-Performance LOWESS Smoothing for R

[thisisamirv]

Submitting Author Name: Amir Valizadeh
Submitting Author Github Handle: @thisisamirv
Other Package Authors Github handles:
Repository: https://github.com/thisisamirv/lowess-project
Sub-directory: bindings/r
Version submitted: 1.3.0
Submission type: Stats
Badge grade: gold
Editor: TBD
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

Package: rfastlowess
Title: High-Performance LOWESS Smoothing for R
Version: 1.3.0
Authors@R: person("Amir", "Valizadeh", email = "thisisamirv@gmail.com", role = c("aut", "cre", "fnd"), comment = c(ORCID = "0000-0001-5983-8527"))
Description: Provides high-performance LOWESS smoothing (Locally Weighted 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.
License: MIT + file LICENSE | Apache License (== 2.0)
SystemRequirements: 'Rust' programming language (via 'rustup' or system tools)
Encoding: UTF-8
Roxygen: list(markdown = TRUE, roclets = c("namespace", "rd", "srr::srr_stats_roclet"))
biocViews: Preprocessing, StatisticalMethod, Regression, FunctionalPrediction, QualityControl, KEGG
Imports: BiocGenerics
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
BugReports: https://github.com/thisisamirv/lowess-project/issues
URL: https://github.com/thisisamirv/lowess-project, https://lowess.readthedocs.io/
Config/rextendr/version: 0.5.0
Config/roxygen2/version: 8.0.0

Scope

• Please indicate which of our statistical package categories this package falls under. (Please check one or more appropriate boxes below):

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses
  • Probability Distributions

Pre-submission Inquiry

• A pre-submission inquiry has been approved in issue#<739>

General Information

• Who is the target audience and what are scientific applications of this package?
  The target audience includes statisticians, bioinformaticians, and data scientists. Scientific applications include genomic data smoothing, time series analysis, and general-purpose robust trend estimation and filtering.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

  • The first implementation of a novel algorithm; or
  • The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or
  • An improvement on other implementations of similar algorithms in R.

  Please include hyperlinked references to all other relevant software.

  Response:
  This package is an improvement on other implementations of similar algorithms in R, specifically the base R stats::lowess and Python's statsmodels.nonparametric.smoothers_lowess.lowess.

  It improves upon existing implementations by offering:

  1. Speed: Average of 2-3x faster than R's lowess and up to 327x faster than Python's statsmodels via a compiled, multi-threaded Rust backend.
  2. Algorithmic Robustness: Implements Median Absolute Deviation (MAD) for scale estimation rather than the standard median of absolute residuals (MAR), providing a breakdown-point-optimal estimator. It also introduces boundary padding policies (Extend, Reflect, Zero) to remove asymmetric bias near the edges.
  3. Feature Completeness: Includes features absent in base R, such as streaming/online modes, confidence and prediction intervals, k-fold cross-validation, auto-convergence, and 7 distinct kernel options.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

  Response: Not applicable. This is a general-purpose mathematical modeling and statistical smoothing tool. It does not contain or require human subject datasets.

Badging

• What grade of badge are you aiming for? (bronze, silver, gold)

  Gold

• If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)

  This package fulfills the requirements for a Gold badge by satisfying the following aspects:

  1. Performance Enhancements via Compiled Code: The core computations are pushed to a memory-safe, multi-threaded Rust backend (via extendr), allowing for highly parallelized execution and significantly improved execution speed over traditional R/C implementations.
  2. Algorithmic Extensions: It vastly extends the baseline LOWESS algorithm by incorporating native support for prediction and confidence interval calculations, multiple robustness weighting and boundary padding policies, and automated cross-validation, moving it far beyond a simple wrapper.
  3. Larger-than-RAM / Streaming capabilities: The package implements streaming and online processing modes for LOWESS smoothing, allowing it to efficiently calculate and update estimations on large out-of-core datasets (such as long-running time-series or heavy genomic sequences) without memory exhaustion.

Technical checks

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
• I/we have read the Statistical Software Peer Review Guide for Authors.
• I/we have run autotest checks on the package, and ensured no tests fail.
• The srr_stats_pre_submit() function confirms this package may be submitted.
• The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.

Use of Generative AI

• Generative AI tools were used to produce some of the material in this submission.

Description of AI Usage

Generative AI (Antigravity, an agentic AI assistant) was used in different aspects of the production and maintenance of this software. Specifically:

• Design of the logo for the software.
• Writing the foundation of the bindings scripts.
• Identification and patching of security vulnerabilities (CVEs) in dependency stacks and hardening of FFI layers (Node.js, WASM).
• Maintaining complex Makefiles and ensuring cross-platform build consistency.
• Fixing broken workspace tests and improving test coverage.
• Research and help find different alternative solutions to performance aspects of the software.
• Design of different workflows for maintenance and release of the software.
• Debugging and organizing the codebase.

Affirmation: All AI-generated material has been carefully reviewed, tested, and approved by the human authors. Mathematical algorithms were specifically written by the author, cross-checked with the references, and validated for different scenarios.

Publication options

• Do you intend for this package to go on CRAN?
• Do you intend for this package to go on Bioconductor?

Code of conduct

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

[ropensci-review-bot]

Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type @ropensci-review-bot help for help.

[ropensci-review-bot]

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Editor check started

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---

Matches are generated by the 'pkgmatch' package, based on inverse-term frequencies; see the main function documentation for details.

[ropensci-review-bot]

Checks for rfastlowess (v1.2.0)

git hash: af37ea78

NOTE: R package is in the 'bindings/r' sub-directory

• ✔️ Package name is available
• ✔️ has a 'contributing' file.
• ✔️ uses 'roxygen2'.
• ✔️ 'DESCRIPTION' has a URL field.
• ✔️ 'DESCRIPTION' has a BugReports field.
• ✔️ Package has at least one HTML vignette
• ✔️ All functions have examples.
• ✖️ Repository has no website
• ✔️ Package has continuous integration checks.
• ✔️ Package coverage is 100%.
• ✔️ All goodpractice linters passed.
• ✔️ This is a statistical package which complies with all applicable standards
• ✔️ R CMD check found no errors.
• ✔️ R CMD check found no warnings.

Important: All failing checks above must be addressed prior to proceeding

Package License: MIT + file LICENSE | Apache License (== 2.0)

---

1. rOpenSci Statistical Standards (srr package)

This package is in the following category:

• Regression and Supervised Learning

✔️ All applicable standards [v0.2.0] have been documented in this package (148 complied with; 45 N/A standards)

Click to see the report of author-reported standards compliance of the package with links to associated lines of code, which can be re-generated locally by running the srr_report() function from within a local clone of the repository.

---

2. Package Dependencies

Details of Package Dependency Usage (click to open)

The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.

type	package	ncalls
internal	base	33
internal	rfastlowess	7
internal	graphics	2
imports	BiocGenerics	NA
suggests	BiocStyle	NA
suggests	knitr	NA
suggests	rmarkdown	NA
suggests	testthat	NA
linking_to	NA	NA

Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.

base

list (9), as.double (3), c (3), do.call (3), emptyenv (3), new.env (3), args (2), lapply (2), as.integer (1), get (1), if (1), is.null (1), parent.frame (1)

rfastlowess

env_args (3), coerce_nullable (1), Lowess (1), OnlineLowess (1), StreamingLowess (1)

graphics

lines (2)

NOTE: No imported packages appear to have associated function calls; please ensure with author that these 'Imports' are listed appropriately.

---

3. Statistical Properties

This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.

Details of statistical properties (click to open)

The package has:

• code in C (1% in 1 files), R (45% in 9 files), Rust (49% in 1 files) and TOML (6% in 4 files)
• 1 authors
• 1 vignette
• no internal data file
• 1 imported package
• 9 exported functions (median 8 lines of code)
• 24 non-exported functions in R (median 4 lines of code)
• 35 R functions (median 3 lines of code)

---

Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
The following terminology is used:

• loc = "Lines of Code"
• fn = "function"
• exp/not_exp = exported / not exported

All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown() function

The final measure (fn_call_network_size) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.

measure	value	percentile	noteworthy
files_R	9	68.6
files_src	6	94.4
files_vignettes	1	72.7
files_tests	1	67.3
loc_R	367	63.0
loc_src	450	46.6
loc_vignettes	70	19.9
loc_tests	4	6.6
num_vignettes	1	68.8
n_fns_r	33	78.8
n_fns_r_exported	9	62.9
n_fns_r_not_exported	24	86.2
n_fns_src	35	90.7
n_fns_per_file_r	5	81.1
n_fns_per_file_src	3	83.4
num_params_per_fn	2	39.2
loc_per_fn_r	6	42.0
loc_per_fn_r_exp	8	45.1
loc_per_fn_r_not_exp	4	56.6
loc_per_fn_src	3	76.1
doclines_per_fn_exp	19	42.1
doclines_per_fn_not_exp	0	0.0	TRUE
doclines_per_fn_src	2	98.5	TRUE
fn_call_network_size	6	54.4

---

3a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

---

4. goodpractice and other checks

Details of goodpractice checks (click to open)

3a. Continuous Integration Badges

![ci.yml](https://github.com/thisisamirv/lowess-project/actions/workflows/ci.yml"><img src="https://github.com/thisisamirv/lowess-project/actions/workflows/ci.yml/badge.svg)

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
23127643940	CI	success	af37ea	190	2026-03-16
23127643943	R-CMD-check	success	af37ea	30	2026-03-16

---

3b. goodpractice results

R CMD check with rcmdcheck

R CMD check generated the following note:

1. checking compiled code ... NOTE
   File ‘rfastlowess/libs/rfastlowess.so’:
   Found non-API call to R: ‘R_NamespaceRegistry’

Compiled code should not call non-API entry points in R.

See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual,
and section ‘Moving into C API compliance’ for issues with the use of
non-API entry points.

Test coverage with covr

Package coverage: 100

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
validate_params	16
validate_common_args	15

---

Package Versions

package	version
pkgstats	0.2.2.20
pkgcheck	0.1.2.294
srr	0.1.4.45

---

Editor-in-Chief Instructions:

Processing may not proceed until the items marked with ✖️ have been resolved.

This package has some noteworthy properties, see 'Package Statistics' details above.

[maurolepore]

Thanks @thisisamirv! Happy to see the full stats submission.

We'll continue the search for a handling editor.

[thisisamirv]

Thank you @maurolepore. I noticed one of the checks did not pass (✖️ Repository has no website). I am working on that one, plus some other improvements for the R binding. I will very soon publish a version 1.3.0 and will let you know.

[thisisamirv]

@maurolepore The new version (v1.3.0) is available now with multiple under the hood improvements and fixes.

[thisisamirv]

@ropensci-review-bot check package

[ropensci-review-bot]

Thanks, about to send the query.

[ropensci-review-bot]

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Editor check started

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[maurolepore]

@ropensci-review-bot check package