Calculate Forest Dynamics

Determines the dynamics of tree species communities (mortality rates, recruitment, loss and gain in basal area, net changes and turnover). Important notes are a) The 'forest_df' argument (data) must contain the columns 'plot' (plot identification), 'spp' (species identification), DBH_1 (Diameter at breast height in first year of measure) and DBH_2 (Diameter at breast height in second year of measure). DBH_1 and DBH_2 must be numeric values; b) example input file in 'data(forest_df_example)'; c) The argument 'inv_time' represents the time between inventories, in years; d) The 'coord' argument must be of the type 'c(longitude, latitude)', with decimal degree values; e) Argument 'add_wd' represents a dataframe with wood density values (g cm-3) format with three columns ('genus', 'species', 'wd'). This argument is set to NULL by default, and if isn't provided, the wood density will be estimated with the getWoodDensity() function from the 'BIOMASS' package.

Spatial Dependence: Weighting Schemes, Statistics

A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) and multicoloured join count statistics, 'APLE' ('Li 'et al.' ) , local 'Moran's I', 'Gearys C' ('Anselin' 1995) and 'Getis/Ord' G ('Ord' and 'Getis' 1995) , 'saddlepoint' approximations ('Tiefelsdorf' 2002) and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') . The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) , with further extensions in 'Bivand' (2022) . 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) , as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) . A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) and 'Bivand et al.' (2017) was added in 1.3-7. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.

Microsoft(r)-Inspired Color Palettes

Provide color schemes for maps (and other graphics) based on the color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2.

Add Ipea Editorial Standards to 'ggplot2' Graphics

Convenient functions to create 'ggplot2' graphics following the editorial guidelines of the Institute for Applied Economic Research (Ipea).

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!

'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.

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.

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.