Implements linear and generalized linear models for provider profiling,
    incorporating both fixed and random effects. For large-scale providers, the linear 
    profiled-based method and the SerBIN method for binary data 
    reduce the computational burden. Provides post-modeling features, such as 
    indirect and direct standardization measures, hypothesis testing, 
    confidence intervals, and post-estimation visualization. 
    For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.

Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.

Stochastic block model used for dynamic graphs represented by Poisson processes.
    To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
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