marglik: NEGATIVE LOG-LIKELIHOOD ASSUMING INDEPEDENCE WITHIN CLUSTERS
In weightedScores: Weighted Scores Method for Regression Models with Dependent Data

Negative log-likelihood assuming independence within clusters.

1	marglik(param,xdat,ydat,margmodel,link)

`param`	The vector of regression and not regression parameters.
`xdat`	(\mathbf{x}_1 , \mathbf{x}_2 , … , \mathbf{x}_n )^\top, where the matrix \mathbf{x}_i,\,i=1,…,n for a given unit will depend on the times of observation for that unit (j_i) and will have number of rows j_i, each row corresponding to one of the j_i elements of y_i and p columns where p is the number of covariates including the unit first column to account for the intercept. This xdat matrix is of dimension (N\times p), where N =∑_{i=1}^n j_i is the total number of observations from all units.
`ydat`	(y_1 , y_2 , … , y_n )^\top, where the response data vectors y_i,\,i=1,…,n are of possibly different lengths for different units. In particular, we now have that y_i is (j_i \times 1), where j_i is the number of observations on unit i. The total number of observations from all units is N =∑_{i=1}^n j_i. The ydat are the collection of data vectors y_i, i = 1,…,n one from each unit which summarize all the data together in a single, long vector of length N.
`margmodel`	Indicates the marginal model. Choices are “poisson” for Poisson, “bernoulli” for Bernoulli, and “nb1” , “nb2” for the NB1 and NB2 parametrization of negative binomial in Cameron and Trivedi (1998).
`link`	The link function. Choices are “log” for the log link function, “logit” for the logit link function, and “probit” for the probit link function.