Probabilistic Support Vector Machines

Implements kernel-based classification Support Vector Machines with reliable estimated probabilities of class membership. Theoretical support for the functions in this package can be found in Duarte Silva (2025) .

Research Assessment Tools

Includes algorithms to assess research productivity and patterns, such as the h-index and i-index. Cardoso et al. (2022) Cardoso, P., Fukushima, C.S. & Mammola, S. (2022) Quantifying the internationalization and representativeness in research. Trends in Ecology and Evolution, 37: 725-728.

High Dimensional Discriminant Analysis

Performs linear discriminant analysis in high dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection.

Test and Detection of Explosive Behaviors for Time Series

Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) . Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.

R as a Plotting Engine

Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary: (1) The graph parameters file, which name is passed to the function 'rplotengine()'. The user can specify the titles, choose the type of the graph, graph output formats (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend, whether to show or not a grid at the background, etc. (2) The data to be plotted, which name is specified as a parameter ('data_filename') in the previous file. This data file has a tabulated format, with a single character (e.g. tab) between each column. Optionally, the file could include data columns for showing confidence intervals.

Converting Transport Data from GTFS Format to GPS-Like Records

Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in 'data.table' format. It also has some functions to subset GTFS data in time and space and to convert both representations to simple feature format.

Efficient Estimation Under Staggered Treatment Timing

Efficiently estimates treatment effects in settings with randomized staggered rollouts, using tools proposed by Roth and Sant'Anna (2023) .

Conesa Colors Palette

Provides a collection of palettes designed to integrate with 'ggplot', reflecting the color schemes associated with 'ConesaLab'.

Lightweight JSON Parsing and Serialization

Converts between JSON text and ordinary R vectors and lists through a small, predictable set of functions, backed by vendored 'yyjson' < https://github.com/ibireme/yyjson> and requiring no system JSON library. Parsing accepts character, raw and file input and reports failures through structured conditions; serialization writes UTF-8 bytes suitable for use directly as an HTTP request body. The type mapping is deliberately narrow and fully documented, so what goes in and what comes out are both predictable.

National Road Safety Observatory (ONSV) Styles for 'gt' Tables

Wrapper functions for customizing HTML tables from the 'gt' package to the ONSV style.