Projects

EACS

EACS

Estatística Aplicada à Ciência do Solo. This package contains documented datasets and reproducible data analysis from research in Soil Science.
Main collaborators: Milson Evaldo Serafim, Fábio Benedito Ono, among others.

RDASC

RDASC

Reproducible Data Analysis from Scientitic Collaborations. This packages contains documented datasets and reproducible data analysis from collaborations with researchers of many areas.
Main collaborators: Larissa May de Mio, Paulo Lichtemberg.

gammacount

gammacount

A package for modelling dispersed count data with the Gamma-Count distribution. It contains functions to generate random numbers from the Gamma Count distribuition and also will provide functions to fit regression models.
Project owner: Eduardo Elias Ribeiro Jr.

labestData

labestData

Contains more than 450 fully documented datasets for learning and teaching of data analysis. These datasets came from books and were copied to the package as data frames with the correponding documentation provided in the book source.
Project owner: PET Estatística UFPR.

nlmSet

nlmSet

Nonlinear regression models catalogue. This objective of this project is develop a R package with a Shiny interface to document and explore nonlinear regression models. The model curve can be manipulated using sliders, so allowing the user select the most apropriate model for applications.
Team: Augusto Buzzoni Calcanhoto, Willian Ramos & Bruna Wundervald.

electric-spacing

electric-spacing

An GNU Emacs minor-mode to automatically add spacing around operators for R (ess-mode). This minor-mode automatically applies the convention of using spaces around operators in R, it follows the R coding style.

wzRfun

wzRfun

This package contains functions that were developed for analysis and representation of data in addition to other general-purpose tasks. The package name has a very obvious motivation except for the fact that I prefer to think about Rfun as R is fun and not R functions.

mcglm

mcglm

Multivariate covariance generalized linear models. mcglm fits multivariate covariance generalized linear models. It allows using a different linear predictor for each response variable of a multivariate response vector. The response variable can be continous or dicrete, like counts and binary and also limited continuos ou discrete/continuous inflated responses. The most important and relevant feature is that many covariance structures can be used to model the relations among variables.
Project owner: Wagner Hugo Bonat.

Tikz

Tikz

This is my personal gallery of drawings done with Tikz latex package. These drawings ilustrates concepts in Statistics, program flows, logos and graphics.