A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization  (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.

ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models.   Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).

Fits linear models with endogenous regressor using latent instrumental variable approaches. 
    The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as 
    Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method 
    that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
    method of moment approach that deals with endogeneity in a multilevel setting.
    These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
    See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>.
    Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.