'RStudio' Addins for Show Outline of a R Markdown/'LaTeX' Project

'RStudio' allows to show and navigate for the outline of a R Markdown file, but not for R Markdown projects with multiple files. For this reason, I have developed several 'RStudio' addins capable of show project outline. Each addin is specialized in showing projects of different types: R Markdown project, 'bookdown' package project and 'LaTeX' project. There is a configuration file that allows you to customize additional searches.

Colombian Macro-Financial Time Series Generator

This repository aims to contribute to the econometric models' production with Colombian data, by providing a set of web-scrapping functions of some of the main macro-financial indicators. All the sources are public and free, but the advantage of these functions is that they directly download and harmonize the information in R's environment. No need to import or download additional files. You only need an internet connection!

Calibration of Binary and Multiclass Probabilities

Provides S3 calibrators, metrics, and diagnostics for binary and multiclass probability calibration. Binary methods include Platt scaling, temperature scaling, beta calibration, histogram binning, and isotonic regression. Multiclass methods include temperature scaling, vector scaling, Dirichlet calibration, and a one-vs-rest wrapper for the binary calibrators. A calibration-inference layer adds debiased calibration errors, bootstrap confidence intervals, and a kernel calibration hypothesis test for binary and multiclass predictions, including the strong (canonical) multiclass case. Methods follow Platt (1999, ISBN:9780262194488), Zadrozny and Elkan (2002) , Guo et al. (2017) < https://proceedings.mlr.press/v70/guo17a.html>, Kull et al. (2017) , Kull et al. (2019) , Widmann et al. (2019) , and Kumar et al. (2019) .

Download Indicators from UNICEF Data Warehouse

An R client to fetch SDMX (Statistical Data and Metadata eXchange) CSV series from the UNICEF Data Warehouse < https://data.unicef.org/>. Part of a trilingual suite also available for 'Python' and 'Stata'. Features include automatic pagination, caching with memoisation, country name lookups, metadata versioning (vintages), and comprehensive indicator support for SDG (Sustainable Development Goals) monitoring.

Interval Estimation of Linear Models with Heteroskedasticity

Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at < https://raw.githubusercontent.com/prdm0/hcci/master/references/dissertacao_mestrado.pdf>. The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) , while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) . The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) . This bootstrap scheme is known as weighted or wild bootstrap.

Open Welfare Data Brazil

Tools for collecting municipal-level data < http://www.transparencia.gov.br/swagger-ui.html> from several Brazilian governmental social programs.

Spatial Regression Analysis

A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) . The models are further described by 'Anselin' (1988) . Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) and (1999) are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) , and model fitting methods by 'Bivand' and 'Piras' (2015) ; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) . 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation .

Core Inflation

Provides access to core inflation functions. Four different core inflation functions are provided. The well known trimmed means, exclusion and double weighing methods, alongside the new Triple Filter method introduced in Ferreira et al. (2016) < https://goo.gl/UYLhcj>.

Download, Compile and Link 'OpenBLAS' Library with R

The 'ropenblas' package (< https://prdm0.github.io/ropenblas/>) is useful for users of any 'GNU/Linux' distribution. It will be possible to download, compile and link the 'OpenBLAS' library (< https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the 'GNU/Linux' distribution used. With the 'ropenblas' package it is possible to download, compile and link the latest version of the 'OpenBLAS' library even the repositories of the 'GNU/Linux' distribution used do not include the latest versions of 'OpenBLAS'. If of interest, older versions of the 'OpenBLAS' library may be considered. Linking R with an optimized version of 'BLAS' (< https://netlib.org/blas/>) may improve the computational performance of R code. The 'OpenBLAS' library is an optimized implementation of 'BLAS' that can be easily linked to R with the 'ropenblas' package.

Decision Models with Multi Attribute Utility Theory

Provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities for a set of indexes, compute utilities and weights for the whole decision tree defining the decision model and simulate weights employing Dirichlet distributions under addition constraints in weights. Also includes other rating analysis methods as for example the Colley, Offensive - Defensive ratings and the ranking aggregation with Borda count.