Programmatically access the 'Quickbase' JSON API <https://developer.quickbase.com>. 
    You supply parameters for an API call, 'qbr' delivers an http request to the 
    API endpoint and returns its response. Outputs follow 'tidyverse' philosophy.

Provides a 'brms'-like interface for fitting Bayesian regression models 
    using 'INLA' (Integrated Nested Laplace Approximations) and 'TMB' (Template Model Builder). 
    The package offers faster model fitting while maintaining familiar 'brms' syntax and 
    output formats. Supports fixed and mixed effects models, multiple probability distributions, 
    conditional effects plots, and posterior predictive checks with summary methods compatible 
    with 'brms'. 'TMB' integration provides fast ordinal regression capabilities. Implements 
    methods adapted from 'emmeans' for marginal means estimation and 'bayestestR' for Bayesian 
    inference assessment. Methods are based on Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>, 
    Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, Lenth (2016) <doi:10.18637/jss.v069.i01>, 
    Bürkner (2017) <doi:10.18637/jss.v080.i01>, Makowski et al. (2019) <doi:10.21105/joss.01541>, 
    and Kruschke (2014, ISBN:9780124058880).

An extensive set of functions to perform Qualitative Comparative Analysis:
       crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA')
       and fuzzy sets ('fsQCA'). 'QCA' is a methodology that bridges
       the qualitative and quantitative divide in social science research.
       It uses a Boolean minimization algorithm, resulting in a minimal causal
       configuration associated with a given phenomenon.

Clustered set-relational data in Qualitative Comparative Analysis
    (QCA) can have a hierarchical structure, a panel structure or repeated cross
    sections. 'QCAcluster' allows researchers to supplement the analysis
    of pooled the data with a differentiated perspective focusing on selected 
    partitions of the data. The pooled data can be partitioned along the 
    dimensions of the clustered data (individual cross sections or time series) 
    to perform partition-specific truth table minimization. Empirical 
    researchers can further calculate the weight that each partition has on the 
    parameters of the pooled solution and the diversity of the cases under 
    analysis within and across partitions 
    (see <https://ingorohlfing.github.io/QCAcluster/>).