Introduction | Installation (GitHub) | Load the package | Example
Package: TensorMCMC 0.1.0
Ritwick Mondal
TensorMCMC: Tensor Regression with Stochastic Low-Rank Updates
Provides methods for low-rank tensor regression with tensor-valued predictors and scalar covariates. Model estimation is performed using stochastic optimization with random-walk updates for low-rank factor matrices. Computationally intensive components for coefficient estimation and prediction are implemented in C++ via 'Rcpp'. The package also includes tools for cross-validation and prediction error assessment.
Authors:
TensorMCMC_0.1.0.tar.gz
TensorMCMC_0.1.0.tar.gz(r-4.7-arm64)TensorMCMC_0.1.0.tar.gz(r-4.7-x86_64)TensorMCMC_0.1.0.tar.gz(r-4.6-arm64)TensorMCMC_0.1.0.tar.gz(r-4.6-x86_64)
TensorMCMC_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
TensorMCMC/json (API)
| # Install 'TensorMCMC' in R: |
| install.packages('TensorMCMC', 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:03dae74c2a. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 150 | ||
| linux-devel-x86_64 | OK | 137 | ||
| source / vignettes | OK | 233 | ||
| linux-release-arm64 | OK | 146 | ||
| linux-release-x86_64 | OK | 156 | ||
| wasm-release | OK | 127 |
Exports:cv.tensor.reggetmeangetmean_cpppredict_tensor_cpppredict_tensor_regrigammarmsetensor.regupdate_beta_cpp
Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simple rank comparison via in-sample RMSE | cv.tensor.reg |
| posterior mean for tensor regression | getmean |
| Posterior Mean Using C++ | getmean_cpp |
| Predict Response Using Tensor Regression C++ | predict_tensor_cpp |
| Predict tensor regression (wrapper) | predict_tensor_reg |
| Prediction from tensor regression (S3 method) | predict.tensor.reg |
| Inverse-gamma random number generator | rigamma |
| root-mean-square error (RMSE) | rmse |
| Tensor Regression using Rcpp | tensor.reg |
| Update Beta Matrices Using C++ Random Walk | update_beta_cpp |