### 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

*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

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

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

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

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

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

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

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