Provides tools for dyadic multilevel modeling with linear and
    generalized linear mixed-effects models. It validates and prepares
    long-format cross-sectional and intensive longitudinal data, including
    ecological momentary assessment designs, for distinguishable and
    exchangeable dyads. It also supports datasets containing multiple observed
    dyad compositions. It constructs composition-aware,
    model-ready variables for Actor-Partner Interdependence Models (APIMs),
    Dyadic Score Models (DSMs), and Dyad-Individual Models (DIMs). Prepared data
    can be used with model engines such as 'glmmTMB' and 'brms' for Gaussian and
    non-Gaussian outcomes, including counts, proportions, and skewed continuous
    responses. Post-estimation tools compare compatible fitted models and
    back-transform exchangeable sum-and-difference random-effect covariance
    structures into member-level quantities. The APIM and DSM specifications
    and their relationships follow Iida et al. (2018)
    <doi:10.1177/0265407517725407>; the multilevel sum-and-difference
    random-effects implementation for exchangeable dyads adapts del Rosario
    and West (2025) <doi:10.1177/25152459251351286>.

Implements the Dyad Ratios algorithm for estimating latent
    variables from time-series survey data. The algorithm estimates a
    latent mood dimension (or two dimensions) from a set of issue opinion
    series. Supports annual, quarterly, monthly, and daily aggregation
    intervals, optional exponential smoothing, and up to two latent
    dimensions. Input data can be provided as a data frame or read from
    delimited text files. Based on Stimson's 'MCalc' C++ program. 
    See Stimson (2018) <doi:10.1177/0759106318761614> for more details.

Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>), the multilevel p2 model (Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>, the (multilevel) b2 model.