rigamma

Inverse-gamma random number generator

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

rigamma function

<dl><dt>n</dt>
<dd>Number of samples.</dd>
<dt>shape</dt>
<dd>Shape parameter of the gamma distribution.</dd>
<dt>rate</dt>
<dd>Rate parameter of the gamma distribution.</dd></dl>

Arguments

Inverse-gamma random number generator — rigamma

<dl>

<dt>n</dt>
<dd>Number of samples.</dd>


<dt>shape</dt>
<dd>Shape parameter of the gamma distribution.</dd>


<dt>rate</dt>
<dd>Rate parameter of the gamma distribution.</dd>

</dl>

rigamma: Inverse-gamma random number generator

Description

Usage

Value

Arguments