A Procedure to Clean, Decompose and Aggregate Timeseries

Clean, decompose and aggregate univariate time series following the procedure "Cyclic/trend decomposition using bin interpolation" and the Logbox method for flagging outliers, both detailed in Ritter, F.: Technical note: A procedure to clean, decompose, and aggregate time series, Hydrol. Earth Syst. Sci., 27, 349–361, , 2023.

Automatic Description of Factorial Analysis

Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).

Landscape Epidemiology and Evolution

A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) .

A Toolbox for Writing Pretty Papers and Reports

A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based reports and to prettify the output of various estimated models.

Parallel Programming Tools for 'Rcpp'

High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial "for" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.

Examples using 'RcppClassic' to Interface R and C++

The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatibility in the package 'RcppClassic'). This package 'RcppClassicExamples' provides usage examples for the older, deprecated API. There is also a corresponding package 'RcppExamples' with examples for the newer, current API which we strongly recommend as the basis for all new development.

Latent Factor Mixed Models

Fast and accurate inference of gene-environment associations (GEA) in genome-wide studies (Caye et al., 2019, ). We developed a least-squares estimation approach for confounder and effect sizes estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several times faster than the existing GEA approaches, then our previous version of the 'LFMM' program present in the 'LEA' package (Frichot and Francois, 2015, ).

Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Shared Memory Multithreading

This project extends 'R' with a mechanism for efficient parallel data access by utilizing 'C++' shared memory. Large data objects can be accessed and manipulated directly from 'R' without redundant copying, providing both speed and memory efficiency.

Paginate the HTML Output of R Markdown with CSS for Print

Use the paged media properties in CSS and the JavaScript library 'paged.js' to split the content of an HTML document into discrete pages. Each page can have its page size, page numbers, margin boxes, and running headers, etc. Applications of this package include books, letters, reports, papers, business cards, resumes, and posters.