feols_multiverse

# a loop-based function in r
# that can be used for multiverse analysis 
# with the feols function from the fixest package

feols_multiverse <- function(dep_var, indep_var, cont_var, back_end){
        
        # function to create the powerset
        power <- function(x){
                
                sets <- lapply(1:(length(x)), function(i) combn(x, i, simplify = F))
                unlist(sets, recursive = F)
                
        }
        
        # create the powerset
        powerset <- c(" ", as.list(paste0(" + ", lapply(power(cont_var), 
                                                        function(x) sprintf(paste0(x, collapse = " + "))))))
        
        # create the df of calls
        df.calls <- do.call(rbind, lapply(powerset, as.data.frame))
        names(df.calls)[1] <- "set.element"
        
        
        calllist <- list()
        
        for(i in 1:(length(dep_var))){
                
                df.calls$call <- paste0("feols(", dep_var[i], " ~ ", indep_var, df.calls$set.element, back_end[i])
                df.calls$dv <- dep_var[i]
                
                calllist[[i]] <- df.calls # add it to the list
                
        }
        
        # bind the list together as a tibble
        df.calls <- do.call(rbind, calllist)
        df.calls <- subset(df.calls, select = c("call", "dv"))
        
        # create a list to be filled with regression results
        reglist = list()
        
        # loop the regression runs
        for(i in seq(df.calls$call)){
        
                skip_to_next <- FALSE
                tryCatch(
                expr = {
                
                model <- broom::tidy(eval(rlang::parse_expr(df.calls$call[i])))
                model$formula <- df.calls$call[i]
                model$dv <- df.calls$dv[i]
                
                reglist[[i]] <- model # add it to the list
                
                }, 
                        
                error = function(x) {skip_to_next <<- TRUE}
                        
                )
                        
                if(skip_to_next) { next } 
        }
        
        # bind the list together as a tibble
        regresion_results <- do.call(rbind, reglist)
        
        # print the results
        regresion_results
        
}