New simulation functionality

Summary of larger updates:

• Added support for simulating shares for a set of alternatives given an estimated model: simulateShares(). This is similar to the predict() function in mlogit.
• Removed support for using an estimated preference space model as an input in the options() function. I found this just far too confusing, and instead encourage users to supply a WTP space model with the computed WTP from a preference space model as starting parameters.

Summary of smaller updates:

• Updated the summary() and main logitr() functions to keep the basic information (run #, log-likelihood value, number of iterations, and output status) whenever numMultistarts > 1. Previously this information was only kept if keepAllRuns was set to TRUE.

Updates to options and a few small bug fixes

Summary of larger updates:

• I got rid of the logitr.summary() function and instead added the logitr class to all the models and renamed the summary function to summary.logitr(). Now you can just use the standard summary() function to summarize model results.
• I finally fixed the analytic gradient for WTP space MXL models. I tested analytic versus numeric for WTP space and Preference Space MXL models and they are all identical, including variations of using normally and log-normally distributed parameters.
• Added startParBounds as an argument in options.

Smaller updates:

• Changed the summary() function to print more digits in the
  summary table.
• Rounded printing of the elapsed time in the summary table.
• Forced the sigma values in MXL models to be positive using abs(). Negative values for sigma parameters should not be an issue because the standard normal is symmetric.
• Changed the summary of random parameters to show "summary of 10k draws"
• Updated hessian to always use numeric approx for SE calculation since it's faster.
• Made scaleInputs default to TRUE.

Bugs fixed:

• If the prefSpaceModel was a multistart, it was grabbing the correct
  bestModel for the WTP calculations, but not the logLik value. Now it's
  getting the right logLik value too.
• Fixed a bug with the scaling option where it was blowing up to use scaling numbers.

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.