Description: Dynamic bindings to the Qt library for calling Qt methods
and extending Qt clases from R. Other packages build upon
qtbase to provide special-purpose high-level interfaces to

Description: Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTET).  The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables), (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package).

Description: Provides several methods for computing the Quantile Treatment
Effect (QTE) and Quantile Treatment Effect on the Treated (QTET). The main cases
covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as
randomly assigned after conditioning on some covariates (also called conditional
independence or selection on observables), (iii) Identification is based on a
Difference in Differences assumption (several varieties are available in the
Imports: Hmisc, parallel, quantreg, BMisc, formula.tools, ggplot2,

Description: Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2017) <https://ssrn.com/abstract=3013341>, Callaway, Li, and Oka (2017) <arXiv:1702.03618>).

Description: Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <https://ssrn.com/abstract=3013341>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).