Provides correlation-based penalty estimators for both linear and logistic regression models by implementing a new regularization method that incorporates correlation structures within the data. This method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. See Tutz and Ulbricht (2009) <doi:10.1007/s11222-008-9088-5> and Algamal and Lee (2015) <doi:10.1016/j.eswa.2015.08.016>.

Implements the covariate balancing propensity score (CBPS) proposed
    by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is
    estimated such that it maximizes the resulting covariate balance as well as the
    prediction of treatment assignment. The method, therefore, avoids an iteration
    between model fitting and balance checking.  The package also implements optimal
    CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>,  
    several extensions of the CBPS beyond the cross-sectional, binary treatment setting.
    They include the CBPS for longitudinal settings so that it can be used in 
    conjunction with marginal structural models from Imai and Ratkovic (2015) 
    <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment 
    variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) 
    <DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of 
    covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation 
    with multiple distinct binary treatments administered simultaneously. In the future 
    it will be extended to other settings including the generalization of experimental 
    and instrumental variable estimates.

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.

The Central Bank of the Republic of Turkey (CBRT) provides one of 
  the most comprehensive time series databases on the Turkish economy. The 'CBRT' 
  package provides functions for accessing the CBRT's electronic data delivery 
  system <https://evds2.tcmb.gov.tr/>. It contains the lists of all data 
  categories and data groups for searching the available variables (data series).  
  As of November 3, 2024, there were 40,826 variables in the dataset. The lists 
  of data categories and data groups can be updated by the user at any time. A 
  specific variable, a group of variables, or all variables in a data group can 
  be downloaded at different frequencies using a variety of aggregation methods.