R Observatory: Metrics for TensorPreAve version 1.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

TensorPreAve

1.1.0

Rank and Factor Loadings Estimation in Time Series Tensor Factor Models

0packages depend

3.1Kdownloads / year

–test coverage

11/13checks pass

Overview

About

Maintained by Weilin ChenFirst published 2022-11-082 releasesCRAN page ↗GitHub ↗

A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) arXiv:2208.04012 for more details.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("TensorPreAve", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("William-Chenwl/TensorPreAve")

Development version from William-Chenwl/TensorPreAve. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-tensorpreave

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-TensorPreAve

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-TensorPreAve

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

2NOTE11OK

Failing flavors

NOTE r-devel-linux-x86_64-debian-clang
NOTE r-devel-linux-x86_64-debian-gcc

Slowest check: 2.3 min · r-oldrel-windows-x86_64

Code health

None

Tests · ratio 0.00

not tracked

Coverage

100%

Documentation · exports

4

Dependencies · direct

Check history

NOTE2026-03-10
12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMENoVignettesYes · dynamicpkgdown siteNoNEWSNoCode of conductNoContributing guideNo

Examples that run: 83%
Documented parameters: 94%
Return-value docs: 83%
References docs: 25%

Downloads

3.1K

CRAN downloads in the past year

Rank #13,398 · ~8/day · ~257/mo

Releases R versions

Daily download trend is not available in this view yet.

22430 days

76890 days

3.1K1 year

Compare downloads with other packages →

Also on122 r2u14 autocran50 c2d4u

Repository

GitHub ↗ william-chenwl/tensorpreave

1Stars

0Forks

0Open issues

0Open PRs

0Releases

8Commits

1Contributors

8 commits · Last activity 2023-04-16

Stars over time

2024-03-13 · 12026-07-07 · 1

Repository practices

Upstream repositoryBeta

2 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/william-chenwl/tensorpreave on 2026-08-09.

CRAN release process (1)

cran-comments.md

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

4 external dependencies (excludes base and recommended)

Depends (1)

R >= 2.10

Imports (4)

rTensorMASSstatspracma

LinkingTo (0)

none

Suggests (2)

knitr rmarkdown

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (1)

Maintainer (1)

Weilin Chen

Author, Maintainer

Authors (1)

Weilin Chen

Author, Maintainer

Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
1.1.0Latest
2023-04-14 · current release · diff ↗
0.1.1
2022-11-08
R
R 4.2.0 released · 2022-04-22

Package metadata

First published: 2022-11-08
Total releases: 2 / 4 yrs
License: GPL-3 OSI
Minimum R: ≥ 2.10
Bundled data: 874 KB / 2 files
Download size: 903 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Appears in task views

Time Series Analysis

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("TensorPreAve")

Format

Chen, W. (2023). TensorPreAve: Rank and Factor Loadings Estimation in Time Series Tensor Factor Models (Version 1.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.TensorPreAve

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for TensorPreAve version 1.1.0 [Data set]. HJJB, LLC. Data release v2026-08-13. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-13, which the citation names so these numbers can be found later. More on citing and the projects behind them.

Report a problem with this page →