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#' Designing, random assigning and evaluating Randomized Control Trials
#'
#' RCT provides three important group of functions:
#' a) functions for pre-processing the design of the RCT
#' b) Functions for assigning treatment status and checking for balances
#' c) Function for evaluating the impact of the RCT
#'
#' @docType package
#' @name RCT
#' @details
#' RCT helps you focus on the statistics of the randomized control trials,
#' rather than the heavy programming lifting.
#' RCT helps you in the whole process of designing and evaluating a RCT.
#' 1. Clean and summarise the data in which you want to randomly assign treatment
#' 2. Decide the share of observations that will go to control group
#' 3. Decide which variables to use for strata building
#' 4. Robust Random Assignment by strata/blocks
#' 5 Impact evaluation of all y's and heterogeneities
#' To lean more about RCT, start with the vignette: browseVignettes(package = "RCT")
#' @section RCT functions:
#' treatment_assign: Robust treatment assign by strata/blocks
#'
#' impact_eval: Automatized impact evaluation with heterogeneous treatment effects
#'
#' balance_table: Balance tables for any length of covariates
#'
#' balance_regression: LPM of treatment status against covariates with F-test
#'
#' tau_min: Computation of the minimum detectable effect between control & treatment units
#'
#' tau_min_probability: Computation of the minimum detectable effect between control & treatment units for dichotomous y-vars
#'
#' summary_statistics: Summary statistics of all numeric columns in your data
#'
#' ntile_label: Rank and divide observations in n groups, with label
#' @references
#' Athey, Susan, and Guido W. Imbens (2017) "The Econometrics Randomized Experiments". Handbook of economic field experiments. \url{https://arxiv.org/abs/1607.00698}
#' @seealso
#' Useful links:
#' https://github.com/isidorogu/RCT
#' Report bugs at https://github.com/isidorogu/RCT/issues
#' @author Isidoro Garcia Urquieta, isidoro.gu@@gmail.com
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