Package: FLLat 1.2-1
Gen Nowak
FLLat: Fused Lasso Latent Feature Model
Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV). Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample. Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
Authors:
FLLat_1.2-1.tar.gz
FLLat_1.2-1.tar.gz(r-4.5-noble)FLLat_1.2-1.tar.gz(r-4.4-noble)
FLLat_1.2-1.tgz(r-4.4-emscripten)FLLat_1.2-1.tgz(r-4.3-emscripten)
FLLat.pdf |FLLat.html✨
FLLat/json (API)
NEWS
| # Install 'FLLat' in R: |
| install.packages('FLLat', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- simaCGH - Simulated aCGH Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:716ac34e2f. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 03 2024 |
| R-4.5-linux-x86_64 | OK | Sep 03 2024 |
Exports:FLLatFLLat.BICFLLat.FDRFLLat.PVE
Dependencies:bitopscaToolsgplotsgtoolsKernSmooth
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fused Lasso Latent Feature Model | FLLat |
| Optimal Tuning Parameters for the Fused Lasso Latent Feature Model | FLLat.BIC |
| False Discovery Rate for the Fused Lasso Latent Feature Model | FLLat.FDR plot.FDR |
| Choosing the Number of Features for the Fused Lasso Latent Feature Model | FLLat.PVE plot.PVE |
| Plots Results from the Fused Lasso Latent Feature Model | plot.FLLat |
| Predicted Values and Weights based on the Fused Lasso Latent Feature Model | predict.FLLat |
| Simulated aCGH Data | simaCGH |