Package: RCT
Title: Assign Treatments, Power Calculations, Balances, Impact
Evaluation of Experiments
Version: 1.0.3
Authors@R: person("Isidoro", "Garcia-Urquieta", email = "isidoro.gu@gmail.com",
role =c("aut", "cre"))
Description: Assists in the whole process of designing and evaluating Randomized Control Trials.
Robust treatment assignment by strata/blocks, that handles misfits;
Power calculations of the minimum detectable treatment effect
Balance tables of T-test of covariates;
Balance Regression: (treatment ~ all x variables) with F-test of null model;
Impact_evaluation: Impact evaluation regressions. This function
gives you the option to include control_vars, fixed effect variables,
cluster variables (for robust SE), multiple endogenous variables and
multiple heterogeneous variables (to test treatment effect heterogeneity)
summary_statistics: Function that creates a summary statistics table with statistics
rank observations in n groups: Creates a factor variable with n groups. Each group has
a min and max label attach to each category.
Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: dplyr, purrr, glue, rlang, tidyr, stringr, MASS, pracma, lfe,
broom, forcats, magrittr, ggplot2, utils, tidyselect (>= 1.0.0)
Suggests: knitr, rmarkdown, testthat, qpdf
RoxygenNote: 7.1.0
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-07-13 02:15:34 UTC; Isidoro
Author: Isidoro Garcia-Urquieta [aut, cre]
Maintainer: Isidoro Garcia-Urquieta <isidoro.gu@gmail.com>
Repository: CRAN
Date/Publication: 2020-07-13 05:10:02 UTC

Package: RCT
Title: Assign Treatments, Power Calculations, Balances, Impact
Evaluation of Experiments
Version: 1.1.1
Authors@R: person("Isidoro", "Garcia-Urquieta", email = "isidoro.gu@gmail.com",
role =c("aut", "cre"))
Description: Assists in the whole process of designing and evaluating Randomized Control Trials.
Robust treatment assignment by strata/blocks, that handles misfits;
Power calculations of the minimum detectable treatment effect or minimum populations;
Balance tables of T-test of covariates;
Balance Regression: (treatment ~ all x variables) with F-test of null model;
Impact_evaluation: Impact evaluation regressions. This function
gives you the option to include control_vars, fixed effect variables,
cluster variables (for robust SE), multiple endogenous variables and
multiple heterogeneous variables (to test treatment effect heterogeneity)
summary_statistics: Function that creates a summary statistics table with statistics
rank observations in n groups: Creates a factor variable with n groups. Each group has
a min and max label attach to each category.
Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.
License: GPL-2
Encoding: UTF-8
Imports: dplyr, purrr, glue, rlang, tidyr, stringr, MASS, pracma, lfe,
broom, forcats, magrittr, ggplot2, utils, tidyselect (>= 1.0.0)
Suggests: knitr, rmarkdown, testthat, qpdf
RoxygenNote: 7.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-08-02 03:24:05 UTC; isidorogarcia
Author: Isidoro Garcia-Urquieta [aut, cre]
Maintainer: Isidoro Garcia-Urquieta <isidoro.gu@gmail.com>
Repository: CRAN
Date/Publication: 2021-08-02 04:20:08 UTC