rmse

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.

Ritwick Mondal

TensorMCMC

Tensor Regression with Stochastic Low-Rank Updates

rmse function

<dl><dt>a</dt>
<dd>Predicted values.</dd>
<dt>b</dt>
<dd>True observed values.</dd></dl>

Arguments

root-mean-square error (RMSE) — rmse

<dl>

<dt>a</dt>
<dd>Predicted values.</dd>


<dt>b</dt>
<dd>True observed values.</dd>

</dl>

rmse: root-mean-square error (RMSE)

Description

Usage

Value

Arguments