Maximum Likelihood estimation of random utility discrete choice models (logit and probit).

Changes since version 0-1.0

```
* 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.
```

Changes since version 0.1-2

```
* 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
```

Changes since version 0.1-3

```
* 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.
```

Changes since version 0.1-4

```
* 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
```

Changes since version 0.1-5

```
* 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
```

Changes since version 0.1-6

```
* a bug in mFormula (effects vs variable) is fixed
```

Changes since version 0.1-7

```
* 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
```

Changes since version 0.1-8

```
* 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
```

Changes since version 0.2-0

```
* all the rda files are now compressed
```

Changes since version 0.2-1

```
* 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
```

Changes since version 0.2-2

```
* some sys.frame() changed to parent.frame()
```

Changes since version 0.2-3

```
* 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
```

Changes since version 0.2-4

```
* 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
* group-hetheroscedastic model can be estimated by setting the
relevant covariates in the 4th part of the formula
* 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
* zbu and zbt distributions are added : these are
one-parameter distributions for which the lower bond is 0.
* there was a bug in the triangular distribution which is now
fixed
* bug in the effects method fixed
* a new iv function is provided
* the linear predictor is now returned
```