rnormmat

Create a matrix or data frame of defined dimensions, whose columns are random normal vectors

Machine learning and visualization package with an 'S7' backend featuring
comprehensive type checking and validation, observable runs, and auditable outputs.
The functional API provides one-call access to supervised and unsupervised learning
through train(), cluster(), and decomp(). All configuration steps are performed using
setup functions and validated. A single call to train() can handle preprocessing,
decomposition, hyperparameter tuning, model stacking, and testing with nested resampling.
Supports 'data.frame', 'data.table', and 'tibble' inputs and parallel execution. Plot
methods for trained and resampled supervised models draw interactive figures through
'rtemis.draw'. Includes native implementations of SuperLearner, RuleFit, and Linear
Additive Trees, and supports conformal prediction. The package first appeared in
E.D. Gennatas (2017)
<https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Efstathios D. Gennatas

rtemis

Schema-Driven Machine Learning and Visualization

E.D. Gennatas

rnormmat function

<dl><dt>nrow</dt>
<dd>Integer: Number of rows.</dd>
<dt>ncol</dt>
<dd>Integer: Number of columns.</dd>
<dt>mean</dt>
<dd>Float: Mean.</dd>
<dt>sd</dt>
<dd>Float: Standard deviation.</dd>
<dt>return_df</dt>
<dd>Logical: If TRUE, return data.frame, otherwise matrix.</dd>
<dt>seed</dt>
<dd>Integer: Set seed for <code>rnorm</code>.</dd></dl>

Arguments

Author

Random Normal Matrix — rnormmat

<dl>

<dt>nrow</dt>
<dd>Integer: Number of rows.</dd>


<dt>ncol</dt>
<dd>Integer: Number of columns.</dd>


<dt>mean</dt>
<dd>Float: Mean.</dd>


<dt>sd</dt>
<dd>Float: Standard deviation.</dd>


<dt>return_df</dt>
<dd>Logical: If TRUE, return data.frame, otherwise matrix.</dd>


<dt>seed</dt>
<dd>Integer: Set seed for <code>rnorm</code>.</dd>

</dl>

rnormmat: Random Normal Matrix

Description

Usage

Value

Arguments

Author

Examples