Lightweight Reproducible Reporting

Order, create and store reports from R. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans.

Downloading, Reading and Analyzing POF Microdata - Package in Development

Provides tools for downloading, reading and analyzing the POF, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.

Robust Garch(1,1) Model

A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) .

Analysis Functions of Respiratory Data

Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data, calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) ), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' ) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) ) were applied for the analysis of respiratory data.

Fitting GLMs with Missing Data in Both Responses and Covariates

Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) , Maiti and Pradhan (2009) , Maity, Pradhan, and Das (2019) for further methodological details.

Data Sets from Mixed-Effects Models in S

Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000).

Data Sets for Psychometric Modeling

Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) , Culpepper, S. A., & Balamuta, J. J. (2021) , Yinghan Chen et al. (2021) , Yinyin Chen et al. (2020) , Culpepper, S. A. (2019a) , Culpepper, S. A. (2019b) , Culpepper, S. A., & Chen, Y. (2019) , Culpepper, S. A., & Balamuta, J. J. (2017) , and Culpepper, S. A. (2015) .

Forest Structure Extrapolation with R

Set of tools to streamline the modeling of the relationship between satellite imagery time series or any other environmental information, such as terrain elevation, with forest structural attributes derived from 3D point cloud data and their subsequent imputation over the broader landscape.

Downloading, Reading and Analyzing PNS Microdata

Provides tools for downloading, reading and analyzing the National Survey of Health - PNS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.

Downloading, Reading and Analyzing PNAD COVID19 Microdata

Provides tools for downloading, reading and analyzing the COVID19 National Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.