  This paper describes the Difference-of-Log-Normals (DLN) distribution. A
companion paper makes the case that the DLN is a fundamental distribution in
nature, and shows how a simple application of the CLT gives rise to the DLN in
many disparate phenomena. Here, I characterize its PDF, CDF, moments, and
parameter estimators; generalize it to N-dimensions using spherical
distribution theory; describe methods to deal with its signature
``double-exponential'' nature; and use it to generalize growth measurement to
possibly-negative variates distributing DLN. I also conduct Monte-Carlo
experiments to establish some properties of the estimators and measures
described.
