rtruncnormRcpp

Sample from a truncated normal distribution. Samples are drawn
componentwise, so each component of the vector is allowed its own
mean, standard deviation, and upper and lower limits. The components
are assumed to be independent.

internal

For Bayesian and classical inference and prediction with count-valued data,
Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable,
and easy-to-use approach. STAR models the observed count data using a rounded
continuous data model and incorporates a transformation for greater flexibility.
Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of
(i) transforming count-valued data and subsequently
(ii) modeling the transformed data using Gaussian models.
STAR is well-defined for count-valued data, which is reflected in predictive accuracy,
and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion.
Importantly, STAR is easy to combine with existing MCMC or point estimation
methods for continuous data, which allows seamless adaptation of continuous data
models (such as linear regressions, additive models, BART, random forests,
and gradient boosting machines) for count-valued data. The package also includes several
methods for modeling count time series data, namely via warped Dynamic Linear Models.
For more details and background on these methodologies, see the works of
Kowal and Canale (2020) <doi:10.1214/20-EJS1707>,
Kowal and Wu (2022) <doi:10.1111/biom.13617>,
King and Kowal (2022) <arXiv:2110.14790>, and
Kowal and Wu (2023) <arXiv:2110.12316>.

Brian King

countSTAR

Flexible Modeling of Count Data

Dan Kowal

rtruncnormRcpp function

<dl><dt>y_lower</dt>
<dd><code>m x 1</code> vector of lower endpoints</dd>
<dt>y_upper</dt>
<dd><code>m x 1</code> vector of upper endpoints</dd>
<dt>mu</dt>
<dd><code>m x 1</code> vector of conditional expectations</dd>
<dt>sigma</dt>
<dd><code>m x 1</code> vector of conditional standard deviations</dd>
<dt>u_rand</dt>
<dd><code>m x 1</code> vector of uniform random variables</dd></dl>

Arguments

Sample from a truncated normal distribution — rtruncnormRcpp

<dl>

<dt>y_lower</dt>
<dd><code>m x 1</code> vector of lower endpoints</dd>


<dt>y_upper</dt>
<dd><code>m x 1</code> vector of upper endpoints</dd>


<dt>mu</dt>
<dd><code>m x 1</code> vector of conditional expectations</dd>


<dt>sigma</dt>
<dd><code>m x 1</code> vector of conditional standard deviations</dd>


<dt>u_rand</dt>
<dd><code>m x 1</code> vector of uniform random variables</dd>

</dl>

rtruncnormRcpp: Sample from a truncated normal distribution

Description

Usage

Value

Arguments