logitr 0.5.0

• The multistart optimization loop is now parallelized.
• Exported the fquantile() function, which is a faster implementation of the stats::quantile() function.

logitr 0.4.0

Larger changes:

• A new predict.logitr() method was added for making probability and choice predictions from logitr class objects.
• The predictProbs() and predictChoices() functions were depreciated.
• Added new fitted.logitr() and residuals.logitr() methods.
• Added optional predict argument to the main logitr() function which controls whether predicted probabilities, fitted.values, and residuals are included in the returned object. Default setting is TRUE.
• Changed the name of the coefficients vector in the returned object from “coef” to “coefficients” to be consistent with other packages.
• Changed the argument name from “choice” to “outcome” to be more general

Bugs:

• Fixed bug where the returned object contained the scaled data rather than the original, unscaled data

logitr 0.3.1

• Bug fix: Cast X object to matrix for single-parameter models
• Updated the logic for clustering with and without panel data
• Added the se.logitr() method.
• Added the vcov argument to the logitr() function.
• Improved vignette on interaction models with individual-specific variable interactions.

logitr 0.3.0

Breaking changes with v0.2.0:

• Several arguments were moved out of the previous options argument and are now passed directly as arguments to logitr(). These include: numMultiStarts, useAnalyticGrad, scaleInputs, startParBounds, standardDraws, numDraws, startVals. The options argument is now only used for options to control the optimization handled by nloptr().
• Options for keeping all model outputs on a multistart were removed.

Summary of larger updates:

• Added support for panel data in the log-likelihood function and gradients.
• Several argument names in the logitr() function were changed to make them easier to understand: choiceName became choice, obsIDName became obsID, parNames became pars, priceName became price, weightsName became weights, clusterName became cluster. If used, old names will be passed to the new argument names and a warning will be displayed.
• The log-likelihood and gradient functions were overhauled to improve computational efficiency, resulting in substantially faster estimation for all models.
• The following new methods were introduced: print.logitr(), logLik.logitr(), coef.summary.logitr(), vcov.logitr(), terms.logitr()

Summary of smaller updates:

• Improved summary.logitr() and coef.logitr() methods for better printing, now using printCoefmat().
• Added input checks for wtp() and wtpCompare() functions
• Fixed some errors in some of the documentation examples and removed the dontrun commands on all of them.
• Added the altIDName argument to predictChoices() and predictProbs() to preserve the row order of predictions for each alternative in each set of alternatives. Closes issue #13.
• Fixed bug in data encoding where random parameter names were not aligned with encoded data.
• Added input checks for all predict functions.

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.

• Several argument names were changed to make them easier to understand. These include: choiceName –> choice, obsIDName –> obsID, parNames –> pars, priceName –> price, weightsName –> weights, clusterName –> cluster.
• Several arguments were moved out of the previous options argument and are now passed directly as arguments to logitr(). These include: numMultiStarts, useAnalyticGrad, scaleInputs, startParBounds, standardDraws, numDraws, startVals.
• Some minor tweaks to printing methods.

logitr 0.2.2

• Improved summary.logitr() and coef.logitr() methods for better printing, using printCoefmat()
• Added new methods: print.logitr(), logLik.logitr(), coef.summary.logitr(), vcov.logitr()
• Removed option for keeping all model outputs.
• Added input checks for wtp() and wtpCompare() functions
• Fixed some errors in some of the examples and made them all run (removed dontrun commands).

logitr 0.2.1

• Added altIDName argument to predictChoices() and predictProbs() to preserve the row order of predictions for each alternative in each set of alternatives. Closes issue #13.
• Fixed bug in data encoding where random parameter names were not aligned with encoded data.
• Added input checks for all predict functions.

logitr 0.2.0

Summary of larger updates:

• New prediction functions: predictChoices() and predictProbs(), and , depreciated simulateShares().
• Added robust covariance matrix calculations.
• Added support for clustering errors.
• Major modifications to the recodeData() function to improve encoding efficiency.
• Depreciated dummyCode()

Summary of smaller updates:

• Improved documentation across all vignettes for new features.
• Improved explanation of preference space and WTP space utility models in vignettes.

logitr 0.1.5

• Added robust covariance matrix calculations.
• Added support for clustering errors.

logitr 0.1.4

• Added predictChoices() function.
• Added predictShares() function, depreciating simulateShares().

logitr 0.1.3

• Modified the recodeData() and dummyCode() functions for improved speed.
• Updated simulateShares() to work with the automatic dummy coding from the revised recodeData() and dummyCode() functions.
• Added support for simulateShares() to compute shares for multiple sets of alternatives.
• Added tests for encoding functions
• Added covariance matrix to model export

Bugs

• When simulating shares from a WTP model, only accepted a price named “price” rather than something else such as “Price” - fixed this.
• In simulateShares(), the shares were not correctly computed with a WTP space model because price was still being multiplied by -1. This has been corrected.
• Changes to automatic dummy coding were accidentally ignoring factor levels - that’s been fixed.

logitr 0.1.2

• Fixed bug where model with single variable would error due to a matrix being converted to a vector in the standardDraws() function
• Fixed bug in getCatVarDummyNames() - previously used string matching, which can accidentally match with other similarly-named variables.
• Fixed bug in rowsum() where the reorder argument was set to TRUE, which resulted in wrong logit calculations unless the obsID happened to be already sorted.

logitr 0.1.1

• Changed how failures to converge are handled. Previously would continue to run a while loop. Now it fails and records the failure, along with appropriate changes in summary() and coef().
• Re-defined the wtp space utility models as BX - p. Before it was p + BX and p was re-defined as -1*p.
• If tidyverse library is loaded, data frames were getting converted to tibbles, which broke some things. Fixed this by forcing the input data to be a data.frame()

logitr 0.1.0

Summary of larger updates:

• v0.1.0 Submitted to CRAN!

Summary of smaller updates:

• Reduced the length of the title in DESCRIPTION to less than 65 characters.
• Changed package names in title and description to single quotes, e.g: {nloptr} -> ‘nloptr’
• Added reference in description with doi to Train (2009) “Discrete Choice Methods with Simulation, 2nd Edition”.
• Added statements to dummyCode.Rd and statusCodes.Rd
• Added statements to dummyCode.Rd and statusCodes.Rd.
• Updated description for summary.logitr.Rd.
• Modified multiple functions to use message()/warning() instead of print()/cat().
• Added algorithm to the options input, with the default being set to "NLOPT_LD_LBFGS".

Bugs

• Fixed tiny bug in getParTypes() function - previously was not returning the correct parNames for continuous vs. discrete variables.
• Added an input check to make sure the modelSpace argument is either "pref" or "wtp".
• Added an input check to make sure the priceName argument is only used when the modelSpace argument is set to "wtp".

logitr 0.0.5

Summary of larger updates:

• Added support for auto creating interactions among variables
• exported getCoefTable() function

Summary of smaller updates:

• Added new documentation for prepping data:
  • overall structure
  • dummyCode() function
  • handling interactions
• All vignettes proof-read with lots of small changes to examples
• Added a hex sticker

logitr 0.0.4

Weighted models, new dataset, new encoding features

Summary of larger updates:

• Added support for estimating weighted regressions
• Added and improved documentation for new datasets: yogurt, cars_china, cars_us
• Exported the dummyCode() function for automatically creating dummy-coded variables in a data frame.
• Added support for auto dummy-coding categorical variables prior to model estimation
• Major overhaul of documentation using {pkgdown}

Summary of smaller updates:

• Changed license to MIT (after doing a bit of reading up on this)
• Fixed dimension-matching issue with user-provided draws for mixed logit models
• Fixed bug in modelInputs where obsID was not a vector for tibble inputs
• Added placeholder hex sticker

logitr 0.0.3

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.

logitr 0.0.2

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.

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.