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NEWS

• update of the Rout.save files

• roxygenize is re-run, the link tags are suppressed

• minir update, the JSS paper is cited and used in the CITATION file

• major update, mlogit now depends on dfidx, mlogit.data, mFormula and index are deprecated

• the name of the coefficients is changed, i.e. air:income is now income:air

• some numerical disperancies were caused by Rout.save files. Some IGNORE tags are introduced to fix that.

• bug in model.frame : indexing by a factor and not a character to get the relevant subset of id in the index

• a new package version which coincides with the Journal of Statistical Software article

• some bugs fixed in the vignettes (citet/citep replaced by markdown-like commands), the figure is added in the mixed logit vignette

• Rout.save files are added in the test directory

• effects.mlogit now returns a matrix

• the R files for the vignettes are added (they were note while building the package on RForge)

• thank’s to Mallick Hossain, a bug is fixed in the logsum function

• the Cracker, Catsup and Car data set are back in mlogit since AER, flexmix and mlogitBMA run examples based on them.

• the alt vector in the index is now carrefully checked in case of alternative subseting or reference level change.

• the main vignette is improved, writen in markdown and now and split by sections

• the Exercises vignette is splited and is now writen in markdown

• importantly, the Cholesky matrix is now coerced to a vector by rows and note by columns.

• the mlogit function was checked and improved.

• implementation of the computation of the standard deviations of the covariance matrix of the random parameters, using the delta method.

• some data sets are removed

• zbu and zbt distributions are added : these are one-parameter distributions for which the lower bond is 0,

• a logsum function is provided to compute the log-sum or the inclusive utility of a random utility model,

• group-hetheroscedastic model can be estimated by setting the relevant covariates in the 4th part of the formula,

• the linear predictor is now returned by mlogit,

• correlation can still be a boolean, but can also be a character vector if one wants that a subset of the random parameters being correlated.

• the RiskyTransport data set (used in the vignette to illustrate the estimation of the mixed logit model

• the NOx data set (used in the vignette to illustrate the estimation of the multinomial and group-heteroscedastic logit model),

• the JapaneseFDI data set (used in the vignette to illustrate the estimation of the nested logit model)

A new vignette called mlogit2 is added ; this is the draft version of an article submitted to the Journal of Statistical Software ; it is less exhaustive, but better writen thant the original mlogit vignette.

• the id series (one observation per choice situation) was badly constructed, it is now fixed

• the levels of the choice variable are now equalized to the those of the alt variable, allowing the case were some alternatives are never chosen

• mlogit is now able to estimate models with singular matrix of covariates. At the end of model.matrix.mformula, the linear dependent columns of X are removed

• there was a bug in the triangular distribution which is now fixed

• bug in the effects method fixed

• the list of primes used to generate halton sequences was too short, its length has been increased

• halton sequences where used to estimate mixed logit even for the default value of halton (NULL), this has been fixed

• the contribution of each observation to the gradient is not returned as the ‘gradient’ element of mlogit objects

• the distributions are now checked for rpar and an error is returned in case of unknown distribution

• some sys.frame() changed to parent.frame()

• ranked-order models can be now estimated ; a new argument called ‘ranked’ is introduced in mlogit.data which performs the relevant transformation of the data.frame. The estimated model is then a standard multinomial logit model

• multinomial probit model is now estimated by setting the new probit arguments to TRUE

• for the mixed logit model, different draws are now used for each observation

• a predict method is now available for mlogit objects

• a coef method is added which removes the fixed argument

• constPar can now be a named numeric vector. In this case, default starting values are changed according to constPar

• the vcov method for mlogit objects is greatly enhanced.

• mlogit objects now have two elements which indicate the fitted probabilities : fitted is the estimated probability for the outcome and probabilities contains the fitted probabilities for all the alternatives

• mentions to ‘alt’ in the names of the effects is canceled ; moreover, the intercepts are now called altname:(‘intercept’)

• a ‘choice’ attribute is added to mlogit.data objects

• an effects method is provided, which computes the marginal effects of a covariate

• all the rda files are now compressed

• all the models could normally be estimated on unbalanced data

• the three tests are added, i.e. a new scoretest function and specific methods for waldtest and lrtest from the lmtest package

• the model.matrix method for mlogit objects is now exported

• mFormula modified so that models can be updated

• likelihood has been rewriten for the heteroscedastic logit model, the computation is now much faster

• nested logit models with overlapping nests are now supported; nests = “pcl” enables the estimation of the pair combinatorial logit model

• the norm argument is added to rpar

• the logLik argument is now of class logLik

• mlogit.data is modified so that an id argument can be used with data in long shape

• the argument of mlogit.data used to define longitudinal data is now called id.var

• mlogit.lnls is corrected so that the estimation of multinomial models can handle unbalanced data (pb with Reduce)

• the three tests are temporary removed

• a bug in mFormula (effects vs variable) is fixed

• a third part of the formula is added : it concerns alternative specific variables with alternative specific coefficients

• improved presentation for the Fishing dataset.

• a bug (forgotten drop = FALSE) corrected in model.matrix.mFormula

• Electricity and ModeCanada datasets are added

• if the choice variable is not an ordered factor, use as.factor() instead of class() <- “factor”

• cov.mlogit, cor.mlogit, rpar , med, rg, stdev, mean functions are added to extract and analyse random coefficients.

• a panel argument is added to mlogit so that mixed models with repeated observation can be estimated using panel methods or not

• a problem with the weights argument is fixed

• the estimation of nested logit models with a unique elasticity is now possible using un.nest.el = TRUE

• the estimation of nested logit models can now be done with or without normalization depending on the value of the argument unscaled

• mlogit didn’t work when the dependent variable was an ordered factor in a “wide-shaped” data.frame.

• the reflevel argument didn’t work any more in version 0.1-3.

• major change, most of the package has been rewriten

• it is now possible to estimate heteroscedastic, nested and mixed effects logit model

• the package doesn’t depend any more on maxLik but a specific optimization function is provided for efficiency reason

• robust inference is provided with meat and estfunc methods defined for mlogit models.

• subset argument is added to mlogit so that the model may be estimated on a subset of alternatives.

• reflevel argument is added to mlogit which defines the base alternative.

• hmftest implements the Hausman McFadden test for the IIA hypothesis.

• mlogit.data function has been rewriten. It now use the reshape function.

• logitform class is provided to describe a logit model: update, model.matrix and model.frame methods are available.

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