QUnif

sHalton

These functions provide quasi random numbers or space filling or
 low discrepancy sequences in the \(p\)-dimensional unit cube.

multivariate

datagen

math

The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" <doi:10.1016/j.csda.2007.06.021>, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" <doi:10.1111/1467-842X.00089>, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" <doi:10.22237/jmasm/1130803560>, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" <doi:10.1016/j.csda.2006.06.008>, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" <doi:10.18637/jss.v021.i09>, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" <doi:10.1016/j.csda.2009.02.014>, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" <doi:10.1201/b10159>, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" <doi:10.1201/b10159>, Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

Steve Su

GLDEX

Fitting Single and Mixture of Generalised Lambda Distributions

Martin Maechler

Juha Karvanen

Robert King

Benjamin Dean

R Core Team

QUnif function

<dl> <dt>n.max</dt>
<dd>maximal (sequence) number.</dd> <dt>n.min</dt>
<dd>minimal sequence number.</dd> <dt>n</dt>
<dd>number of \(p\)-dimensional points generated in
 <code>QUnif</code>. By default, <code>n.min = 1, leap = 1</code> and
 the maximal sequence number is <code>n.max = n.min + (n-1)*leap</code>.</dd> <dt>base</dt>
<dd>integer \(\ge 2\): The base with respect to which
 the Halton sequence is built.</dd> <dt>min, max</dt>
<dd>lower and upper limits of the univariate intervals.
 Must be of length 1 or <code>p</code>.</dd> <dt>p</dt>
<dd>dimensionality of space (the unit cube) in which points are
 generated.</dd> <dt>leap</dt>
<dd>integer indicating (if \(&gt; 1\)) if the series should be
 leaped, i.e., only every <code>leap</code>th entry should be taken.</dd></dl>

Arguments

Author

Quasi Randum Numbers via Halton Sequences — QUnif

<dl>

 <dt>n.max</dt>
<dd>maximal (sequence) number.</dd>

 <dt>n.min</dt>
<dd>minimal sequence number.</dd>

 <dt>n</dt>
<dd>number of \(p\)-dimensional points generated in
 <code>QUnif</code>. By default, <code>n.min = 1, leap = 1</code> and
 the maximal sequence number is <code>n.max = n.min + (n-1)*leap</code>.</dd>

 <dt>base</dt>
<dd>integer \(\ge 2\): The base with respect to which
 the Halton sequence is built.</dd>

 <dt>min, max</dt>
<dd>lower and upper limits of the univariate intervals.
 Must be of length 1 or <code>p</code>.</dd>

 <dt>p</dt>
<dd>dimensionality of space (the unit cube) in which points are
 generated.</dd>

 <dt>leap</dt>
<dd>integer indicating (if \(&gt; 1\)) if the series should be
 leaped, i.e., only every <code>leap</code>th entry should be taken.</dd>

</dl>

Quasi Randum Numbers via Halton Sequences

QUnif: Quasi Randum Numbers via Halton Sequences

Description

Usage

Value

Arguments

Author

References

Examples