kurt

Calculate univariate kurtosis for a vector or matrix (algorithm G2 in Joanes
&amp; Gill, 1998). Note that, as defined in this function, the expected kurtosis of 
a normally distributed variable is 0 (i.e., not 3).

Statistics

Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications:
Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>.
Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights.
Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>.
Waller, N. G. (2016). Fungible Correlation Matrices:
A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for
Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568.
Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF)
covariance matrix of standardized regression coefficients: theoretical extensions
and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>.
Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.

Niels G Waller

fungible

Psychometric Functions from the Waller Lab

Niels Waller

Justin Kracht

Jeff Jones

Casey Giordano

Hoang V. Nguyen 

kurt function

<dl><dt>x</dt>
<dd>Either a vector or matrix of numeric values.</dd></dl>

Arguments

Author

Calculate Univariate Kurtosis for a Vector or Matrix — kurt

<dl>

<dt>x</dt>
<dd>Either a vector or matrix of numeric values.</dd>

</dl>

kurt: Calculate Univariate Kurtosis for a Vector or Matrix

Description

Usage

Value

Arguments

Author

References

See Also

Examples