Estimate a total causal effect from observational data under 
    linearity and causal sufficiency. The observational data is supposed to 
    be generated from a linear structural equation model (SEM) with independent 
    and additive noise. The underlying causal DAG associated the SEM is required
    to be known up to a maximally oriented partially directed graph (MPDAG), 
    which is a general class of graphs consisting of both directed and 
    undirected edges, including CPDAGs (i.e., essential graphs) and DAGs. Such
    graphs are usually obtained with structure learning algorithms with added 
    background knowledge. The program is able to estimate every identified 
    effect, including single and multiple treatment variables. Moreover, the 
    resulting estimate has the minimal asymptotic covariance (and hence 
    shortest confidence intervals) among all estimators that are based on the 
    sample covariance.

Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) <doi:10.1162/REST_a_00639>.  The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method.

Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.

Predicts cytoplasmic effector proteins using genomic data by searching for motifs of interest using regular expression searches and hidden Markov models (HMM) based in Haas et al. (2009) <doi:10.1038/nature08358>.

Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.

Provide utilities to work with indices of effect size for a wide 
    variety of models and hypothesis tests (see list of supported models using 
    the function 'insight::supported_models()'), allowing computation of and 
    conversion between indices such as Cohen's d, r, odds, etc.
    References: Ben-Shachar et al. (2020) <doi:10.21105/joss.02815>.