Functions for regional frequency analysis using the methods
  of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis:
  an approach based on L-moments".

Linear model functions using permutation tests.

Implementation based on Zhang, Jie & Huang, Kun (2014) <doi:10.4137/CIN.S14021> Normalized ImQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers. Cancer informatics, 13, CIN-S14021.

Data files and a few functions used in the book 
             'Linear Models and Regression with R: An Integrated Approach' 
             by Debasis Sengupta and Sreenivas Rao Jammalamadaka (2019).

Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>,
  ii.  Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>,
  iii. Imdadullah, Aslam, and Saima (2017),
  iv.  Marquardt (1970) <doi:10.2307/1267205>.

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.