Complete Environment for Bayesian Inference

Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).

A Framework for Robust Shiny Applications

An opinionated framework for building a production-ready 'Shiny' application. This package contains a series of tools for building a robust 'Shiny' application from start to finish.

'Lucide' Icons for 'R'

A lightweight wrapper for the 'Lucide' icon library < https://lucide.dev>. Embed beautiful and consistent 'SVG' icons inline in any 'R' web application.

Build Modern Web Applications with 'htmx' and 'plumber2'

A lightweight framework for building server-driven web applications in 'R'. 'htmxr' combines the simplicity of 'htmx' for partial page updates with the power of 'plumber2' for non-blocking HTTP endpoints. Build interactive dashboards and data applications without writing 'JavaScript', using familiar 'R' patterns inspired by 'Shiny'. For more information on 'htmx', see < https://htmx.org>.

Nelson-Aalen Estimator of the Cumulative Hazard in Multistate Models

Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models .

Kaplan-Meier Multiple Imputation for the Analysis of Cumulative Incidence Functions in the Competing Risks Setting

Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 ).

Empirical Transition Matrix

The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 ). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 ).

Statistical Pattern Recognition and daTing using Archaeological Artefacts assemblageS

Statistical pattern recognition and dating using archaeological artefacts assemblages. Package of statistical tools for archaeology. hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4). mapclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021) . seriograph(): Desachy Bruno (2004) . cerardat(): Bellanger Lise, Husi Philippe (2012) .

Clustering and Prediction using Multi-Task Gaussian Processes with Common Mean

An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) , and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) < https://jmlr.org/papers/v24/20-1321.html>. Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented.

Auxiliary Data Package for Our Main Package 'dartR'

Data package for 'dartR'. Provides data sets to run examples in 'dartR'. This was necessary due to the size limit imposed by 'CRAN'. The data in 'dartR.data' is needed to run the examples provided in the 'dartR' functions. All available data sets are either based on actual data (but reduced in size) and/or simulated data sets to allow the fast execution of examples and demonstration of the functions.