logitr (development version)

logitr 1.1.2

logitr 1.1.1

logitr 1.1.0

logitr 1.0.1

logitr 1.0.0

logitr 0.8.0

logitr 0.7.2

logitr 0.7.1

logitr 0.7.0

logitr 0.6.1

logitr 0.6.0

logitr 0.5.1

logitr 0.5.0

logitr 0.4.0

Larger changes:

Bugs:

logitr 0.3.1

logitr 0.3.0

Breaking changes with v0.2.0:

Summary of larger updates:

Summary of smaller updates:

logitr 0.2.7

Added support for panel data in the log-likelihood function and gradients

logitr 0.2.6

Major changes were made to the gradient functions, which dramatically improved computational efficiency. MNL and MXL models in either preference or WTP spaces now use the faster implementation of the logit calculations.

logitr 0.2.5

This version was the first implementation of an alternative approach for computing the logit probabilities, which increased computational speed. Specifically, the formulation was to compute P = 1 / (1 + sum(exp(V - V_chosen)))

logitr 0.2.4

The vcov() method was modified such that it computes the covariance post model estimation. Previously, the covariance matrix was being computed internally in the logitr() function, and vcov() just returned this value, which was computationally much slower.

logitr 0.2.3

Several breaking changes in this version.

logitr 0.2.2

logitr 0.2.1

logitr 0.2.0

Summary of larger updates:

Summary of smaller updates:

logitr 0.1.5

logitr 0.1.4

logitr 0.1.3

Bugs

logitr 0.1.2

logitr 0.1.1

logitr 0.1.0

Summary of larger updates:

Summary of smaller updates:

Bugs

logitr 0.0.5

Summary of larger updates:

Summary of smaller updates:

logitr 0.0.4

Weighted models, new dataset, new encoding features

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logitr 0.0.3

New simulation functionality

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logitr 0.0.2

Updates to options and a few small bug fixes

Summary of larger updates:

Smaller updates:

Bugs fixed:

logitr 0.0.1

Full reboot of logitr!

Long overdue, I decided to give the logitr program a full overhaul. This is the first version that is compiled as a proper R package that can be directly installed from GitHub. This version is much more robust and flexible than the prior, clunky collection of R files that I had previously been using to estimate logit models.