getDist

scalar. Number of observations to be generated.

character string. Select from:<ul>
<li>"Uniform: Continuous Uniform distribution .</li>
<li>"Normal": Normal distribution (default).</li>
<li>"Normal2": Squared Normal distribution (also known as Chi-squared).</li>
<li>"DoubleExp": Double exponential distribution (also known as Laplace distribution).</li>
<li>"DoubleExp2": Double exponential squared distribution from a <code>DoubleExp(0,1)</code>.</li>
<li>"LogNormal": Lognormal distribution.</li>
<li>"Gamma": Gamma distribution.</li>
<li>"Weibull": Weibull distribution.</li>
<li>"t": Student-t distribution.</li>
</ul>

dist

scalar. Expected value of the desired distribution.

scalar. Standard deviation of the desired distribution.

sigma

scalar. Location parameter of the desired distribution. Default 0**.

par.location

scalar. Scale parameter of the desired distribution. Default 1**.

par.scale

scalar. Shape parameter of the desired distribution, Default 1.

par.shape

vector. Overwrite <code>par.location</code>, <code>par.scale</code>, <code>par.shape</code>. Depends on the distribution (default <code>NULL</code>):<ul>
<li>"Uniform: no matter how is defined always gives numbers between 0 and 1.</li>
<li>"Normal": c(location, scale).</li>
<li>"Normal2": c(location, scale).</li>
<li>"DoubleExp": c(location, scale).</li>
<li>"DoubleExp2": c(location, scale).</li>
<li>"LogNormal": c(location, scale).</li>
<li>"Gamma": c(scale, shape).</li>
<li>"Weibull": c(shape, scale).</li>
<li>"t": c(degrees of freedom).</li>
</ul>

dist.par

scalar. positive value that determine the range between two consecutive rounded values.

rounding.factor

Random observations generator selected from several distributions with user defined mean and variance.

The methods discussed in this package are new non-parametric methods
based on sequential normal scores 'SNS' (Conover et al (2017)
<doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
usually time series data, which may occur singly or in batches,
and may be univariate or multivariate. These methods are designed
to detect changes in the process, which may occur as changes in location
(mean or median), changes in scale (standard deviation, or variance), or
other changes of interest in the distribution of the observations,
over the time observed. They usually apply to large data sets,
so computations need to be simple enough to be done in a reasonable
time on a computer, and easily updated as each new observation
(or batch of observations) becomes available. Some examples and more detail
in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.

Luis Benavides

SNSchart

Sequential Normal Scores in Statistical Process Management

Victor Tercero

Jorge Merlo

getDist function

<ul>
<li>For "Lognormal", <code>par.location</code> and <code>par.scale</code> correspond to the location and scale parameters of the normal
 distribution that generales the lognormal. Hence, in this case they are the logmean and
 the logsigma parameters</li>
<li>For "Normal2" and "DoubleExp2", <code>par.location</code> and <code>par.scale</code> correspond
 correspond to the location and scale parameters of the normal and double exponential
 that are used to generates their squared forms.</li>
</ul>

getDist: Random Observations Generator

Description

Usage

Arguments

Value

**Note

Examples