Differences between brglm and logistf packages

Question

I am working with a dashboard database that contains information about publicly traded companies from BOVESPA. Below follows a piece of this database:

My dependent variable is binary (ROIC.MEDIANA) so I started my estimation via logistic panel. As my data is not normal, I opted for a LOGIT estimation.

setwd("C:/Dados/BOVESPA")
getwd() 
require("openxlsx")
dadosbov <- read.xlsx ("BOVESPA.xlsx") # Importando os dados.
dadosbov <- na.omit(dadosbov) # Omitindo NA's.
dadosbov <- as.data.frame(dadosbov)
attach(dadosbov)
View(dadosbov)

Next I decided to rotate the function glm to estimate the data:

reg1 = glm(ROIC.MEDIANA ~ ECV + MARGEM.EBIT + GIRO.ATIVO + 
ALAVANCAGEM.FINANCEIRA + CICLO.FINANCEIRO + CICLO.OPERACIONAL + HHIE + HHIS 
+ CRISE, family=binomial(link="logit"), data=dadosbov)

What returned a Warning:

Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred

The model is estimated, but the coefficients are wrong due to the statistical problem of separation in logistic regression. Researching how to get around the problem I discovered the work of Firth (1993) who makes the same kind of estimation fixing the problem. In R I discovered two packages that perform the corrected estimation: logistf and brglm:

require("logistf")
reg2 = logistf(ROIC.MEDIANA ~ ECV + MARGEM.EBIT + GIRO.ATIVO + 
ALAVANCAGEM.FINANCEIRA + CICLO.FINANCEIRO + CICLO.OPERACIONAL + HHIE + HHIS 
+ CRISE, data=dadosbov)

No mistake or Warning is generated and I can see the results of the model.

require("brglm")
reg3 = brglm(ROIC.MEDIANA ~ ECV + MARGEM.EBIT + GIRO.ATIVO + 
ALAVANCAGEM.FINANCEIRA + CICLO.FINANCEIRO + CICLO.OPERACIONAL + HHIE + HHIS 
+ CRISE, family = binomial(logit), data=dadosbov, method = "brglm.fit")

Generates a Warning, but I can see the results of the model:

Warning message: In fit.proc(x = X, y = Y, Weights = Weights, start = start, etastart = etastart,: Iteration limit reached

My question is: what is the difference between the two packages? The generated estimates differ in relation to the generated coefficients. From what I researched I could not find differences between the two packages that justify the outputs produced by each of them.

As to the Warning relative to brglm, no matter how much I change the function parameters brglm.control, I keep getting the same Warning and the same estimation results.