**Estimation using Sequential Offsetted Regression** (**SOR**)

Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique. Includes support for binary, count, and continuous data.

**Modeling Spatial Poisson and Related Point Processes** (**sppmix**)

Implements classes and methods for modeling spatial point patterns using inhomogeneous Poisson point processes, where the intensity surface is assumed to be analogous to a finite additive mixture of normal components and the number of components is a finite, fixed or random integer. Extensions to the marked inhomogeneous Poisson point processes case are also presented. We provide an extensive suite of R functions that can be used to simulate, visualize and model point patterns, estimate the parameters of the models, assess convergence of the algorithms and perform model selection and checking in the proposed modeling context.

**Finite Mixture Modeling for Raw and Binned Data** (**mixR**)

Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package ‘Rcpp’.

**Simulate Probabilistic Long-Term Effects in Models with Temporal Dependence** (**pltesim**)

Simulate probabilistic long-term effects in models with temporal dependence.

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