Fixed effect Logit with R I would like to perform a Fixed effect logit estimation in R. 
Can someone point out a package that can do the job?
Note: For the time being I'm not really interested in the random effect.
Update;
Essentially I wonder if there is the plm package for a binary response model.
Here is some documentation for the plm package:
http://cran.r-project.org/web/packages/plm/index.html 
http://dss.princeton.edu/training/Panel101.pdf
 A: I'm almost certain that you mean conditional logistic regression. This will estimate the within-group relationship between your independent variables and your binary dependent variable. It's similar to adding a dummy for each individual (as in fixed effects). In which case, use the clogit() function in the survival package that is included with R.
A: There is a new R Package called "bife" that performs Fixed Effects binary logit Models.
https://cran.r-project.org/web/packages/bife/vignettes/howto.html
A: Standard fixed effects assumptions break down in nonlinear models (like logit).  
When you can assume gaussian disturbance, you can model:
$$
y_{it} = X_{it}\beta + u_{it}\\
y_{it} = X_{it}\beta + \alpha_i + \epsilon_{it}\\
y_{it} - \bar{y}_i = (X_{it}-\bar{X}_i) \beta + \alpha_i - \bar\alpha_i + \epsilon_{it} -\bar\epsilon_i\\
y_{it}^{dm}  = X_{it}^{dm} \beta + \epsilon_{it}^{dm}
$$
where $dm$ is the deviation from the mean.  This is the within transformation.  You get rid of all of your time-invariant heterogeneity, and if you can make an argument that $E[\epsilon_{it}|X,\alpha]=0$, then $\beta$ gets a causal interpretation.
Try this math with a logit link functions and you'll see that it all breaks down.  Other approaches exist -- one of the other answerers suggested conditional logit.  
A: If you mean logistic regression, for example, along the lines of
$Y_i\sim\text{Bernoulli}(1,p_i)\,$, with
$\text{logit}(p_i) = X\beta\,$, 
(equivalently, $P[Y_i=1] = \frac{\exp(X\beta)}{1+\exp(X\beta)},$)
then use glm. For example:
glm(y~x1+x2,family=binomial)

There are examples in the help at ?glm.predict, ?infert and ?esoph.
If you mean fitting a logistic curve via least squares, like
$E(Y) = \alpha \cdot(\frac{\exp(\beta_0+\beta_1 x)}{1+\exp(\beta_0+\beta_1 x)})$
then use nls. There's an example of fitting a logistic function in the help at ?nls.
Both are part of the standard R installation. 
If you mean something else, you need to clarify what you want.

I see from this answer that apparently economists use 'fixed effect model' to refer to a conditional logit model, even though it's far from the only fixed effect model involving a logit. Who would have thought.
As ndoogan mentions in one of the other answers, there's a conditional logistic regression model (clogit) in the survival package.
