Exact and approximation algorithms for variable-subset
  selection in ordinary linear regression models.  Either compute all
  submodels with the lowest residual sum of squares, or determine the
  single-best submodel according to a pre-determined statistical
  criterion.  Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.

A collection of tests, data sets, and examples
 for diagnostic checking in linear regression models. Furthermore,
 some generic tools for inference in parametric models are provided.

Robust test(s) for model diagnostics in regression. The current version contains a robust test for functional specification (linearity). The test is based on the robust bounded-influence test by Heritier and Ronchetti (1994) <doi:10.1080/01621459.1994.10476822>.

Non-parametric estimators for casual effects based on longitudinal modified treatment 
  policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, 
  and traditional longitudinal effects. Continuous, binary, and categorical treatments are allowed as well are 
  censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure 
  irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects 
  can be calculated and relative risks and odds ratios may be calculated for binary outcomes.

Contains a suite of shiny applications
    meant to explore linear model inference feature through simulation
    and games.

Computes the implied weights of linear regression models for estimating
     average causal effects and provides diagnostics based on these weights. These
     diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023)
     <doi:10.1093/biomet/asac058> where
     several regression estimators are represented as weighting estimators, in connection
     to inverse probability weighting. 'lmw' provides tools to diagnose
     representativeness, balance, extrapolation, and influence for these models,
     clarifying the target population of inference. Tools are also available to
     simplify estimating treatment effects for specific target populations of interest.