Tag Archives: insightr

Basic Quantile Regression

By Gabriel Vasconcelos Introduction Today we are going to talk about quantile regression. When we use the lm command in R we are fitting a linear regression using Ordinary Least Squares (OLS), which has the interpretation of a model for … Continue reading

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Structural Analisys of Bayesian VARs with an example using the Brazilian Development Bank

By Gabriel Vasconcelos Introduction Vector Autorregresive (VAR) models are very popular in economics because they can model a system of economic variables and relations. Bayesian VARs are receiving a lot of attention due to their ability to deal with larger … Continue reading

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Benford’s Law for Fraud Detection with an Application to all Brazilian Presidential Elections from 2002 to 2018

By Gabriel Vasconcelos and Yuri Fonseca The intuition Let us begin with a brief explanation about Benford’s law and why should it work as a fraud detector method. Given a set of numbers, the first thing we need to do … Continue reading

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BooST series II: Pricing Optimization

By Gabriel Vasconcelos & Yuri Fonseca Introduction This post is the second of a series of examples of the BooST (Boosting Smooth Trees) model. You can see an introduction to the model here and the first example here. Our objective … Continue reading

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Growing Objects and Loop Memory Pre-Allocation

By Thiago Milagres Preallocating Memory This will be a short post about a simple, but very important concept that can drastically increase the speed of poorly written codes. It is very common to see R loops written as follows: This … Continue reading

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BooST series I: Advantage in Smooth Functions

By Gabriel Vasconcelos and Yuri Fonseca Introduction This is the first of a series of post on the BooST (Boosting Smooth Trees). If you missed the first post introducing the model click here and if you want to see the … Continue reading

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BooST (Boosting Smooth Trees) a new Machine Learning Model for Partial Effect Estimation in Nonlinear Regressions

By Gabriel Vasconcelos and Yuri Fonseca   We are happy to introduce our new machine learning method called Boosting Smooth Trees (BooST) (full article here). This model was a joint work with professors Marcelo Medeiros and Álvaro Veiga. The BooST … Continue reading

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Introducing the HCmodelSets Package

By Henrique Helfer Hoeltgebaum Introduction I am happy to introduce the package HCmodelSets, which is now available on CRAN. This package implements the methods proposed by Cox, D.R. and Battey, H.S. (2017). In particular it performs the reduction, exploratory and … Continue reading

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Tuning xgboost in R: Part II

By Gabriel Vasconcelos In this previous post I discussed some of the parameters we have to tune to estimate a boosting model using the xgboost package. In this post I will discuss the two parameters that were left out in … Continue reading

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Different demand functions and optimal price estimation in R

By Yuri Fonseca Demand models In the previous post about pricing optimization (link here), we discussed a little about linear demand and how to estimate optimal prices in that case. In this post we are going to compare three different … Continue reading

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