Estimate the Probability in the Upper Tail of the Aggregate Loss Distribution

Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.

Latent Variable Models for Networks

Latent variable models for network data using fast inferential procedures. For more information please visit: < http://igollini.github.io/lvm4net/>.

Vincent van Gogh Colour Palette Generator

Provides 'ggplot2'-compatible colour palettes inspired by Vincent van Gogh's paintings. Each palette contains five colours, manually selected by hexadecimal values. Includes tools for assessing colour vision deficiency (CVD) accessibility.

Data Leakage Detection Tools for Machine Learning

Provides utilities to detect common data leakage patterns including train/test contamination, temporal leakage, and data duplication, enhancing model reliability and reproducibility in machine learning workflows. Generates diagnostic reports and visual summaries to support data validation. Methods based on best practices from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0387848570).

Call Google's 'Natural Language', 'Cloud Translation', 'Cloud Speech', and 'Cloud Text-to-Speech' APIs

Access Google Cloud machine learning APIs for text and speech tasks. Use the Cloud Translation API for text detection and translation, the Natural Language API to analyze sentiment, entities, and syntax, the Cloud Speech API to transcribe audio to text, and the Cloud Text-to-Speech API to synthesize text into audio files.

Disaggregate Variable Costs

These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.

Indices for Single-Case Research

Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.

Collection of Machine Learning Datasets for Supervised Machine Learning

Contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020) and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.

Allow Misspellings of Length Function

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

Automated Moderated Nonlinear Factor Analysis Using 'M-plus'

Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) . This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.