Analysis of Differential Item Functioning (DIF) for
dichotomous and polytomous items using an iterative hybrid of
(ordinal) logistic regression and item response theory (IRT)
R (>= 2.7.0), MASS, msm, mvtnorm, polycor, sfsmisc, ltm,
Hmisc, Design

R (>= 2.7.0), MASS, msm, mvtnorm, polycor, sfsmisc, ltm,
Hmisc, rms

Logistic Ordinal Regression Differential Item Functioning using
IRT
Analysis of Differential Item Functioning (DIF) for
dichotomous and polytomous items using an iterative hybrid of
ordinal logistic regression and item response theory (IRT).

LOw Rand and sparsE Covariance matrix estimation
Estimate covariance matrices that contain low rank and sparse components

The Lorentz Transform in Relativistic Physics
Addition of velocities in special relativity is not
straightforward: the parallelogram addition law does not apply because
velocity addition is neither commutative nor associative.  This
package furnishes functionality to work with Lorentz transforms
and the gyrogroup structure of three-velocities.  For general
relativity, see the 'schwarzschild' package.

This
package furnishes functionality to work with Lorentz transforms in special
relativity.  It also implements the gyrogroup structure of three-velocities
following Ungar (2006) <doi:10.1088/0143-0807/27/3/L02>. For general
relativity, see the 'schwarzschild' package.

The Lorentz transform in special relativity; also the gyrogroup structure of three-velocities following Ungar (2006)
<doi:10.1088/0143-0807/27/3/L02>.  For general relativity, see the
'schwarzschild' package.

The Lorentz transform in special relativity; also the gyrogroup structure of three-velocities.  Includes active and passive transforms and the ability to use units in which the speed of light is not one.  For general relativity, see the
'schwarzschild' package.

Tools for Deriving Income Inequality Estimates from Grouped
Income Data
person(given = "Andrew",
family = "Carr",
role = c("aut", "cre", "cph"),
email = "andrew.carr@duke.edu")
Provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census.
These methods use different interpolation techniques to infer the distribution of incomes within income bins.  One method is an implementation of
Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB).  The other method is based on a new technique, Lorenz interpolation,
which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data.  These methods can be used to
estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index.
Jargowsky and Wheeler (2018) <doi:10.1177/0081175018782579>.