Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>).

Insert/extract text "reminders" into/from function source code 
    comments or as the "comment" attribute of any object.  
    The former can be handy in development as reminders of e.g. argument
    requirements, expected objects in the calling environment, required options
    settings, etc. The latter can be used to provide information of the object and 
    as simple manual "tooltips" for users, among other things.

Reference-based multiple imputation of ordinal and binary responses under Bayesian
          framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for 
          missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta 
          Adjustment which can generate tipping point analysis.

Traditional latent variable models assume that the population
    is homogeneous, meaning that all individuals in the population are
    assumed to have the same latent structure. However, this assumption is
    often violated in practice given that individuals may differ in their
    age, gender, socioeconomic status, and other factors that can affect
    their latent structure. The robust expectation maximization (REM)
    algorithm is a statistical method for estimating the parameters of a
    latent variable model in the presence of population heterogeneity as recommended by 
    Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the
    expectation-maximization (EM) algorithm, but it allows for the case
    when all the data are generated by the assumed data generating model.