Bayesian Statistical Tools for Quantitative Proteomics

Bayesian toolbox for quantitative proteomics. In particular, this package provides functions to generate synthetic datasets, execute Bayesian differential analysis methods, and display results as, described in the associated article Marie Chion and Arthur Leroy (2023) .

Statistical Methods and Models for Claims Reserving in General Insurance

Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.

Communication Between Editor and Viewer for Literate Programs

This utility eases the debugging of literate documents ('noweb' files) by patching the synchronization information (the '.synctex(.gz)' file) produced by 'pdflatex' with concordance information produced by 'Sweave' or 'knitr' and 'Sweave' or 'knitr' ; this allows for bilateral communication between a text editor (visualizing the 'noweb' source) and a viewer (visualizing the resultant 'PDF'), thus bypassing the intermediate 'TeX' file.

Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data

Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: to study the relationships between blocks and to identify subsets of variables of each block which are active in their relationships with the other blocks. This package allows to (i) run R/SGCCA and related methods, (ii) help the user to find out the optimal parameters for R/SGCCA such as regularization parameters (tau or sparsity), (iii) evaluate the stability of the RGCCA results and their significance, (iv) build predictive models from the R/SGCCA. (v) Generic print() and plot() functions apply to all these functionalities.

The Conditional Probability Function of a Competing Event

Permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function). See .

Robust Exponential Decreasing Index

Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) < https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>. The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way.

Parse 'Tableau' Workbooks into Functional Data

High-performance parsing of 'Tableau' workbook files into tidy data frames and dependency graphs for other visualization tools like R 'Shiny' or 'Power BI' replication.

Load 'Overture' Datasets as 'dbplyr' and 'sf'-Ready Data Frames

An integrated R interface to the 'Overture' API (< https://docs.overturemaps.org/>). Allows R users to return 'Overture' data as 'dbplyr' data frames or materialized 'sf' spatial data frames.

Read and Write ODS Files

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

Soundscape Background Noise, Power, and Saturation

Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2014) and Burivalova (2017) .