3.5.3.1 Cumulative Distribution Functions (CDF)

Name	Brief	Example
	Computes beta cumulative distribution function at \(x\), with parameters \(a\) and \(b\).
	Computes the lower tail, upper tail and point probabilities in given value \(k\), associated with a Binomial distribution using the corresponding parameters in \(n\), \(p\).
	Computes the lower tail probability for the bivariate Normal distribution.
	Computes the lower tail probability for the \(\chi^2\) distribution with real degrees of freedom.
	Evaluates the cumulative Normal distribution function \(P(x)=\frac 1{\sqrt{2\pi }}\int_{-\infty }^xe^{\frac{-u^2}2}du\).
	Evaluates an approximate value for the complement of the cumulative normal distribution function.
	The error function (or normal error integral).
	The approximate value for the complement of the error function.
	Return value of the inverse of the complementary error function for specified y.
	Return value of the inverse error function for specified y.
	Computes \(F\) cumulative distribution function at \(x\), with parameters\( a\) and\( b\), and lower tail.
	Computes the lower tail probability for the Folded Normal distribution.
	Computes the lower tail probability for the gamma distribution with real degrees of freedom, with parameters\( \alpha\) and \(\beta\) .
	Computes the lower tail probabilities in given value , associated with a hypergeometric distribution using the corresponding parameters in ,nand .
	Computes the cumulative density for the Landau distribution at x and with location parameter mu and scale parameter sigma.
	Computes the lower tail probability for the Lognormal cumulative distribution with parameters \( \mu\) and \(\sigma\).
	Computes the cdf with the lower tail of the non-central beta distribution.
	Computes the probability associated with the lower tail of the non-central \(\chi^2\) distribution.
	Computes the probability associated with the lower tail of the non-central \(\digamma\) or variance-ratio distribution.
	Computes the lower tail probability for the non-central Student's t-distribution.
	Computes the lower tail probability for the normal cumulative distribution.
	Computes the lower tail probabilities in given value \(k\), associated with a Poisson distribution using the corresponding parameters in \(\lambda\).
	Computes the Probability Density (for a normal distribution) integrated from -x to +x.
	Computes the probability associated with the lower tail of the distribution of the Studentized range statistic.
	Computes the cumulative distribution function of Student's t-distribution.
	computes the low tail Weibull cumulative distribution function for value X using the parameters A and B.