Read and Write 'Apache Parquet' Files

Read and write 'Apache Parquet' files. Whole files are read with a single call, and larger ones can be opened to inspect their schema and read selected columns, row groups, or batches. Built on the bundled C library 'carquet', with no required R package dependencies.

Graphical Visualizations for ROBUST-RCT Risk of Bias Assessments

Provides visual representations of risk-of-bias assessments using the ROBUST-RCT framework, as described in Wang et al. (2025) . The graphical visualization displays both factual evaluation (Step 1) and judgment (Step 2).

National Road Safety Observatory (ONSV) Style for 'ggplot2' Graphics

Helps to create 'ggplot2' charts in the style used by the National Road Safety Observatory (ONSV). The package includes functions to customize 'ggplot2' objects with new theme and colors.

Modeling Complex Longitudinal Data in a Quick and Easy Way

Matching longitudinal methodology models with complex sampling design. It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." < https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.

Extra Features for 'reactable' Package

Enhanced functionality for 'reactable' in 'shiny' applications, offering interactive and dynamic data table capabilities with ease. With 'reactable.extras', easily integrate a range of functions and components to enrich your 'shiny' apps and facilitate user-friendly data exploration.

Generalized Hermite Distribution

Probability functions and other utilities for the generalized Hermite distribution.

Microbiota STability ASsessment via Iterative cluStering

The toolkit 'µSTASIS' has been developed for the stability analysis of microbiota in a temporal framework by leveraging on iterative clustering. Concretely, the core function uses Hartigan-Wong k-means algorithm as many times as possible for stressing out paired samples from the same individuals to test if they remain together for multiple numbers of clusters over a whole data set of individuals. Moreover, the package includes multiple functions to subset samples from paired times, validate the results or visualize the output.

'Base Dos Dados' R Client

An R interface to the 'Base dos Dados' API < https://basedosdados.org/docs/api_reference_python/>). Authenticate your project, query our tables, save data to disk and memory, all from R.

Acceptance-Rejection Method for Generating Pseudo-Random Observations

Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), < https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) < https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003) < https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).

Cox MultiBlock Survival

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) < https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) , Philippe Bastien et al. (2005) , Philippe Bastien (2008) , Philippe Bastien et al. (2014) , Kassu Mehari Beyene and Anouar El Ghouch (2020) , Florian Rohart et al. (2017) .