| dsn | R Documentation | 
Density function, distribution function, quantiles and random number generation for the skew-normal (SN) and the extended skew-normal (ESN) distribution.
dsn(x, xi=0, omega=1, alpha=0, tau=0, dp=NULL, log=FALSE)
psn(x, xi=0, omega=1, alpha=0, tau=0, dp=NULL, engine, ...)
qsn(p, xi=0, omega=1, alpha=0, tau=0, dp=NULL, tol=1e-8, solver="NR", ...) 
rsn(n=1, xi=0, omega=1, alpha=0, tau=0,  dp=NULL)
| x | vector of quantiles. Missing values ( | 
| p | vector of probabilities. Missing values ( | 
| xi | vector of location parameters. | 
| omega | vector of scale parameters; must be positive. | 
| alpha | vector of slant parameter(s);  | 
| tau | a single value representing the ‘hidden mean’ parameter 
of the ESN distribution;  | 
| dp | a vector of length 3 (in the SN case) or 
4 (in the ESN case), whose components represent 
the individual parameters described above. If  | 
| n | a positive integer representing the sample size. | 
| tol | a scalar value which regulates the accuracy of the result of 
 | 
| log | logical flag used in  | 
| engine | a character string  which selects the computing engine;
this is either  | 
| solver | a character string which selects the numerical method used for 
solving the quantile equation; possible options are  | 
| ... |  additional parameters passed to  | 
density (dsn), probability (psn), quantile (qsn)
or random sample (rsn) from the skew-normal distribution with given
xi, omega and alpha parameters or from the extended
skew-normal if tau!=0 
Typical usages are
dsn(x, xi=0, omega=1, alpha=0, log=FALSE) dsn(x, dp=, log=FALSE) psn(x, xi=0, omega=1, alpha=0, ...) psn(x, dp=, ...) qsn(p, xi=0, omega=1, alpha=0, tol=1e-8, ...) qsn(x, dp=, ...) rsn(n=1, xi=0, omega=1, alpha=0) rsn(x, dp=)
psn and qsn make use  of function T.Owen
or biv.nt.prob
In qsn, the choice solver="NR" selects the Newton-Raphson method 
for solving the quantile equation, while option solver="RFB"
alternates a step of regula falsi with one of bisection. 
The "NR" method is generally more efficient, but "RFB" is 
occasionally required in some problematic cases.
In version 1.6-2, the random number generation method for rsn has
changed;  the so-called transformation method  (also referred to as the
‘additive representation’) has been adopted for all values of tau.
Also, the code has been modified so that there is this form of consistency:
provided set.seed() is reset similarly before calls, code like
rsn(5, dp=1:3) and rsn(10, dp=1:3), for instance, will start with 
the same initial values in the longer sequence as in the shorter sequence.
The family of skew-normal distributions is an extension of the normal
family, via the introdution of a alpha parameter which regulates
asymmetry; when alpha=0, the skew-normal distribution reduces to 
the normal one.  The density function of the SN distribution 
in the ‘normalized’  case having xi=0 and omega=1 is 
2\phi(x)\Phi(\alpha x), if \phi and \Phi denote the
standard normal density and distribution function.
An early discussion of the skew-normal distribution is given by 
Azzalini (1985); see Section 3.3 for the ESN variant, 
up to a slight difference in the parameterization.
An updated exposition is provided in Chapter 2 of Azzalini and Capitanio (2014); the ESN variant is presented Section 2.2. See Section 2.3 for an historical account. A multivariate version of the distribution is examined in Chapter 5.
Azzalini, A. (1985). A class of distributions which includes the normal ones. Scand. J. Statist. 12, 171-178.
Azzalini, A. with the collaboration of Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.
Functions used by psn:
T.Owen, biv.nt.prob
Related distributions: dmsn, dst, 
dmst
pdf <- dsn(seq(-3, 3, by=0.1), alpha=3)
cdf <- psn(seq(-3, 3, by=0.1), alpha=3)
q <- qsn(seq(0.1, 0.9, by=0.1), alpha=-2)
r <- rsn(100, 5, 2, 5)
qsn(1/10^(1:4), 0, 1, 5, 3, solver="RFB")  
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