The Imputation Uncertainty Principle | The Robustness-Efficiency Tradeoff | Fidelity-Constraint Balance | Example: Shielding Against Corrupted EHR Data
This package is hidden from search results because it is not hosted in the universe of the upstream owner
cran. To fix this you can add URL: https://cran.r-universe.dev/smriti to the package DESCRIPTION file. See also theR-universe documentation.Package: smriti 0.2.0
Xiyuan Guo
smriti: Automated Routing Engine for Longitudinal Missing Data
Provides an automated routing engine for longitudinal missing data. It utilizes a Lagrange-constrained Random Forest based on sample size, missingness rate, and skew to preserve structural variance.
Authors:
smriti_0.2.0.tar.gz
smriti_0.2.0.zip(r-4.7-x86_64)smriti_0.2.0.zip(r-4.7-arm64)smriti_0.2.0.zip(r-4.6-x86_64)smriti_0.2.0.zip(r-4.6-arm64)
smriti_0.2.0.tar.gz(r-4.7-arm64)smriti_0.2.0.tar.gz(r-4.7-x86_64)smriti_0.2.0.tar.gz(r-4.6-arm64)smriti_0.2.0.tar.gz(r-4.6-x86_64)
smriti_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
smriti/json (API)
| # Install 'smriti' in R: |
| install.packages('smriti', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:ed473ff47f. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 119 | ||
| linux-devel-x86_64 | OK | 149 | ||
| source / vignettes | OK | 208 | ||
| linux-release-arm64 | OK | 121 | ||
| linux-release-x86_64 | OK | 132 | ||
| windows-devel-arm64 | OK | 191 | ||
| windows-devel-x86_64 | OK | 134 | ||
| windows-release-arm64 | OK | 205 | ||
| windows-release-x86_64 | OK | 102 | ||
| wasm-release | OK | 123 |
Exports:smriti_fimlsmriti_forestsmriti_imputesmriti_mismriti_micesmriti_ranger
Dependencies:RcppRcppArmadillo
Introduction to smriti: Structural Variance Preservation
Last update: 2026-06-05
Started: 2026-05-21
Outlier-Robust Longitudinal Imputation with smriti
The Outlier Problem in Longitudinal Data | Demonstration: 5% Contamination | Clean Data: All Methods Perform Similarly | Contaminated Data: The Robust Advantage | When to Use robust = TRUE | Integration with Existing Pipelines
Last update: 2026-06-05
Started: 2026-06-05
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| FIML-Smriti Refinement Wrapper | smriti_fiml |
| missForest-Smriti Refinement Wrapper | smriti_forest |
| Smriti Automated Longitudinal Imputation | smriti_impute |
| Smriti Multiple Imputation Wrapper | smriti_mi |
| mice-Smriti Refinement Wrapper | smriti_mice |
| missRanger-Smriti Refinement Wrapper | smriti_ranger |