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extrafont - Tools for Using Fonts

Tools to using fonts other than the standard PostScript fonts. This package makes it easy to use system TrueType fonts and with PDF or PostScript output files, and with bitmap output files in Windows. extrafont can also be used with fonts packaged specifically to be used with, such as the fontcm package, which has Computer Modern PostScript fonts with math symbols.

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10.73 score 9 stars 11 dependents 19k scripts 19k downloads

plsRglm - Partial Least Squares Regression for Generalized Linear Models

Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

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openblascpp

8.60 score 19 stars 7 dependents 149 scripts 1.7k downloads

tester - Tests and Checks Characteristics of R Objects

Allows users to test characteristics of common R objects.

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7.55 score 2 stars 7 dependents 175 scripts 3.2k downloads

survAUC - Estimators of Prediction Accuracy for Time-to-Event Data

Provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data.

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openblas

7.54 score 2 stars 13 dependents 290 scripts 3.0k downloads

ggWebGL - Browser-Native 'WebGL' Rendering for R Graphics

Provides browser-native 'WebGL' rendering for R graphics through 'htmlwidgets'. The package supports grammar-style graphics workflows and renderer-ready specifications for dense analytical and scientific scenes, including point, line, trajectory, raster, vector, mesh, and surface layers, shader-driven display modes, timeline controls, structured views, selection metadata, and publication-oriented static export helpers. Rendering stays in the browser, and the core package remains cross-platform without requiring 'CUDA', 'Metal', or 'OpenCL' toolchains.

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7.44 score 6 stars 1 dependents 32 scripts 394 downloads

bigalgebra - 'BLAS' and 'LAPACK' Routines for Native R Matrices and 'big.matrix' Objects

Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects.

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openblas

7.25 score 4 stars 3 dependents 83 scripts 849 downloads

Cascade - Selection, Reverse-Engineering and Prediction in Cascade Networks

A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

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7.17 score 1 stars 4 dependents 61 scripts 748 downloads

extrafontdb - Holding the Database for the 'extrafont' Package

It is meant to be used with the 'extrafont' package. The 'extrafont' package contains the code to install and use fonts, while the 'extrafontdb' package contains the font database.

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6.89 score 1 stars 12 dependents 267 scripts 16k downloads

Patterns - Deciphering Biological Networks with Patterned Heterogeneous Measurements

A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.

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6.70 score 4 stars 63 scripts 514 downloads

plsRcox - Partial Least Squares Regression for Cox Models and Related Techniques

Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <doi:10.48550/arXiv.1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.

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6.45 score 8 stars 3 dependents 121 scripts 1.6k downloads

Rttf2pt1 - 'ttf2pt1' Program

Contains the program 'ttf2pt1', for use with the 'extrafont' package. This product includes software developed by the 'TTF2PT1' Project and its contributors.

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6.38 score 1 stars 12 dependents 71 scripts 19k downloads

bigPLSR - Partial Least Squares Regression Models with Big Matrices

Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls' and 'widekernelpls') with parity to 'pls'. Optimized for 'bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and <https://hal.science/hal-05352061> highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) <doi:10.1002/(SICI)1099-128X(199701)11:1%3C73::AID-CEM435%3E3.0.CO;2-%23>, Rosipal & Trejo (2001) <https://www.jmlr.org/papers/v2/rosipal01a.html>, Tenenhaus, Viennet, and Saporta (2007) <doi:10.1016/j.csda.2007.01.004>, Rosipal (2004) <doi:10.1007/978-3-540-45167-9_17>, Song, Wang, and Bai (2024) <doi:10.1016/j.chemolab.2024.105238>. Includes kernel logistic PLS with 'C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates.

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openblascpp

6.18 score 1 stars 31 scripts 337 downloads

SelectBoost.gamlss - Stability-Selection via Correlated Resampling for 'GAMLSS' Models

Extends the 'SelectBoost' approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) <https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

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openblascppopenmp

6.10 score 2 stars 20 scripts 277 downloads

bigPCAcpp - Principal Component Analysis for 'bigmemory' Matrices

High performance principal component analysis routines that operate directly on bigmemory::big.matrix() objects. The package avoids materialising large matrices in memory by streaming data through 'BLAS' and 'LAPACK' kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into 'bigmemory'-backed matrices for file-based workflows. Additional interfaces expose 'scalable' singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. 'Scalable' principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) <doi:10.1145/2723372.2751520>.

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openblascpp

6.03 score 9 stars 1 dependents 430 downloads

SelectBoost.FDA - SelectBoost-Style Variable Selection for Functional Data Analysis

Implements 'SelectBoost'-style variable selection workflows for functional data analysis. The package provides FDA-native design and preprocessing objects for raw curves, spline-basis expansions, Functional principal component analysis scores, and scalar covariates; grouped stability-selection routines based on repeated subject-level subsampling; multiple selector backends including lasso, group lasso, and sparse-group lasso; FDA-aware grouping functions and calibration helpers for 'SelectBoost'; method-comparison utilities; a formula interface; simulation, benchmarking, and validation helpers with mapped ground truth; targeted sensitivity-study utilities, two-parameter perturbation-grid workflows, renderer-neutral selection-surface and diagnostic extractors, monotonicity and precision-recall utilities, association diagnostics, report table helpers, and shipped benchmark summaries for mean 'F1' comparisons between FDA-aware and plain 'SelectBoost' workflows; small example datasets; and an optional adapter to the native stability-selection interface from the 'FDboost' package.

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5.85 score 1 stars 35 scripts 304 downloads

boids4R - Reynolds-Style Boids and Swarm Simulation

Provides deterministic two- and three-dimensional boids and swarm simulations for R. The package implements Reynolds-style separation, alignment, and cohesion rules with optional obstacles, attractors, predators, species parameters, and reproducible frame export. Simulation state is renderer-neutral; optional adapters can hand frame data to visualization packages such as 'ggWebGL'. The model follows Reynolds (1987) <doi:10.1145/37402.37406>.

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cpp

5.18 score 1 stars 20 scripts 331 downloads

turner - Turn Vectors and Lists of Vectors into Indexed Structures

Package designed for working with vectors and lists of vectors, mainly for turning them into other indexed data structures.

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5.15 score 1 stars 2 dependents 38 scripts 1.2k downloads

mi4p - Multiple Imputation for Proteomics

A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <doi:10.1371/journal.pcbi.1010420>. It is dedicated to dealing with multiple imputation for proteomics.

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5.10 score 7 stars 36 scripts 388 downloads

SelectBoost.beta - Stability-Selection via Correlated Resampling for Beta-Regression Models

Adds variable-selection functions for Beta regression models (both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm. Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector with an optional standardization speedup, and 'C++' helpers for iterative re-weighted least squares working steps and precision updates. Also provides a fastboost_interval() variant for interval responses, comparison helpers, and a flexible simulator simulation_DATA.beta() for interval-valued data. For more details see Bertrand and Maumy (2023) <doi:10.7490/f1000research.1119552.1>, <https://hal.science/hal-05352047>, and <https://hal.science/hal-05352056>.

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cpp

5.00 score 1 stars 6 scripts 387 downloads

bigANNOY - Approximate k-Nearest Neighbour Search for 'bigmemory' Matrices with Annoy

Approximate Euclidean k-nearest neighbour search routines that operate on 'bigmemory::big.matrix' data through Annoy indexes created with 'RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar metadata from streamed 'big.matrix' rows, supports euclidean, angular, Manhattan, and dot-product Annoy metrics, and can either return in-memory results or stream neighbour indices and distances into destination 'bigmemory' matrices. Explicit index lifecycle helpers, stronger metadata validation, descriptor-aware file-backed workflows, and benchmark helpers are also included.

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cpp

4.98 score 1 stars 27 scripts 394 downloads

SelectBoost - A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

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confidencecorrelationcorrelation-structuremodellingprecisionrecallselection-algorithm

4.98 score 7 stars 3 dependents 15 scripts 462 downloads

plsRbeta - Partial Least Squares Regression for Beta Regression Models

Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

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4.97 score 2 stars 31 scripts 436 downloads

c060 - Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.

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4.94 score 3 stars 48 scripts 347 downloads

Sobol4R - Sobol Indices for Models with Fixed and Stochastic Parameters

Tools to design experiments, compute Sobol sensitivity indices, and summarise stochastic responses inspired by the strategy described by Zhu and Sudret (2021) <doi:10.1016/j.ress.2021.107815>. Includes helpers to optimise toy models implemented in C++, visualise indices with uncertainty quantification, and derive reliability-oriented sensitivity measures based on failure probabilities. It is further detailed in Logosha, Maumy and Bertrand (2022) <doi:10.1063/5.0246026> and (2023) <doi:10.1063/5.0246024> or in Bertrand, Logosha and Maumy (2024) <https://hal.science/hal-05371803>, <https://hal.science/hal-05371795> and <https://hal.science/hal-05371798>.

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cpp

4.88 score 1 stars 8 scripts 231 downloads

bigPLScox - Partial Least Squares for Cox Models with Big Matrices

Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and <https://hal.science/hal-05352061> highlighted fitting and cross-validating PLS-based Cox models to censored big data.

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openblascpp

4.82 score 1 stars 11 scripts 363 downloads

bigKNN - Exact Search and Graph Construction for 'bigmemory' Matrices

Exact nearest-neighbour and radius-search routines that operate directly on 'bigmemory::big.matrix' objects. The package streams row blocks through 'BLAS' kernels, supports self-search and external-query search, exposes prepared references for repeated queries, and can build exact k-nearest-neighbour, radius, mutual k-nearest-neighbour, and shared-nearest-neighbour graphs. Version 0.3.0 adds execution plans, serializable prepared caches, resumable streamed graph jobs, coercion helpers, exact candidate reranking, and recall summaries for evaluating approximate neighbours.

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openblascpp

4.73 score 1 stars 18 scripts 417 downloads

peperr - Parallelised Estimation of Prediction Error

Designed for prediction error estimation through resampling techniques, possibly accelerated by parallel execution on a compute cluster. Newly developed model fitting routines can be easily incorporated. Methods used in the package are detailed in Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062> and were used, for instance, in Porzelius Ch., Schumacher M. and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.

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4.72 score 3 stars 1 dependents 29 scripts 543 downloads

elcf4R - Electricity Load Curves Forecasting at Individual Level

Implements forecasting methods for individual electricity load curves, including Kernel Wavelet Functional (KWF), clustered KWF, Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) models. Provides normalized dataset adapters for iFlex, StoreNet, Low Carbon London, and REFIT; download and read support for IDEAL and GX; explicit Python backend selection for TensorFlow-based LSTM fits; helpers for daily segmentation and rolling-origin benchmarking; and compact shipped example panels and benchmark-result datasets.

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cpp

4.70 score 1 stars 7 scripts 352 downloads

robustfa - Object Oriented Solution for Robust Factor Analysis

Outliers virtually exist in any datasets of any application field. To avoid the impact of outliers, we need to use robust estimators. Classical estimators of multivariate mean and covariance matrix are the sample mean and the sample covariance matrix. Outliers will affect the sample mean and the sample covariance matrix, and thus they will affect the classical factor analysis which depends on the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). So it is necessary to use the robust estimators of the sample mean and the sample covariance matrix. There are several robust estimators in the literature: Minimum Covariance Determinant estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way to make multivariate analysis more robust is to replace the sample mean and the sample covariance matrix of the classical estimators to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) <doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009) <doi:10.18637/jss.v032.i03>), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes.

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4.61 score 1 stars 1 dependents 27 scripts 467 downloads

SelectBoost.quantile - 'SelectBoost'-Style Variable Selection for Quantile Regression

A 'SelectBoost'-inspired workflow for sparse quantile regression. The package builds correlation neighborhoods, perturbs correlated predictors with a directional sampler inspired by the original 'SelectBoost' internals, refits penalized quantile regression models on the perturbed designs, and aggregates variable-selection frequencies across a path of correlation thresholds.

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4.60 score 1 stars 7 scripts 272 downloads