g_cdf

Compute a CDF-based transformation using the observed count data.
The CDF can be estimated nonparametrically or parametrically based on the
Poisson or Negative Binomial distributions. In the parametric case,
the parameters are determined based on the moments of <code>y</code>.
Note that this is a fixed quantity and does not come with uncertainty quantification.

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

g_cdf function

<dl><dt>y</dt>
<dd><code>n x 1</code> vector of observed counts</dd>
<dt>distribution</dt>
<dd>the distribution used for the CDF; must be one of<ul>
<li>"np" (empirical CDF)</li>
<li>"pois" (moment-matched marginal Poisson CDF)</li>
<li>"neg-bin" (moment-matched marginal Negative Binomial CDF)</li>
</ul></dd></dl>

Arguments

Cumulative distribution function (CDF)-based transformation — g_cdf

<dl>

<dt>y</dt>
<dd><code>n x 1</code> vector of observed counts</dd>


<dt>distribution</dt>
<dd>the distribution used for the CDF; must be one of<ul>
<li>"np" (empirical CDF)</li>
<li>"pois" (moment-matched marginal Poisson CDF)</li>
<li>"neg-bin" (moment-matched marginal Negative Binomial CDF)</li>
</ul></dd>

</dl>

g_cdf: Cumulative distribution function (CDF)-based transformation

Description

Usage

Value

Arguments

Examples