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 <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Last updated 2 years ago

7.69 score 16 stars 5 packages 103 scripts 957 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>.

Last updated 2 years ago

6.75 score 1 stars 2 packages 63 scripts 330 downloads

tester - Tests and Checks Characteristics of R Objects

Allows users to test characteristics of common R objects.

Last updated 8 months ago

6.47 score 4 packages 83 scripts 2.0k 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.

Last updated 3 months ago

6.30 score 2 stars 4 packages 179 scripts 2.3k 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.

Last updated 7 months ago

6.16 score 4 stars 18 scripts 514 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.

Last updated 2 years ago

confidencecorrelationcorrelation-structuremodellingprecisionrecallselection-algorithm

5.61 score 7 stars 1 packages 13 scripts 253 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.

Last updated 8 months ago

5.14 score 1 packages 36 scripts 1.3k 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.

Last updated 2 months ago

5.10 score 4 stars 2 packages 52 scripts 668 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 <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.

Last updated 2 years ago

5.09 score 4 stars 2 packages 51 scripts 801 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.

Last updated 2 months ago

4.94 score 6 stars 29 scripts 389 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>.

Last updated 3 years ago

4.38 score 2 stars 1 packages 20 scripts 423 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.

Last updated 2 years ago

4.35 score 3 stars 37 scripts 317 downloads

plsRbeta - Partial Least Squares Regression for Beta Regression Models

Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, <http://journal-sfds.fr/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.

Last updated 2 years ago

4.34 score 2 stars 22 scripts 295 downloads

networkABC - Network Reverse Engineering with Approximate Bayesian Computation

We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.

Last updated 2 years ago

4.34 score 4 stars 11 scripts 212 downloads

BioStatR - Initiation à La Statistique Avec R

Datasets and functions for the book "Initiation à la Statistique avec R", F. Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth edition).

Last updated 2 years ago

4.31 score 4 stars 101 scripts 774 downloads

sageR - Applied Statistics for Economics and Management with R

Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). The first chapter of the book is dedicated to an introduction to statistics and their world. The second chapter deals with univariate exploratory statistics and graphics. The third chapter deals with bivariate and multivariate exploratory statistics and graphics. The fourth chapter is dedicated to data exploration with Principal Component Analysis. The fifth chapter is dedicated to data exploration with Correspondance Analysis. The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. The seventh chapter is dedicated to data exploration with automatic clustering. The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions. The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests. The tenth chapter is dedicated to an Gaussian linear model. The eleventh chapter is dedicated to an introduction to time series. The twelfth chapter is dedicated to an introduction to probit and logit models. Various example datasets are shipped with the package as well as some new functions.

Last updated 2 years ago

4.18 score 2 stars 15 scripts 197 downloads

bootPLS - Bootstrap Hyperparameter Selection for PLS Models and Extensions

Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).

Last updated 2 months ago

3.70 score 1 stars 4 scripts 314 downloads

plsdof - Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression. Model selection for PLS is based on various information criteria (aic, bic, gmdl) or on cross-validation. Estimates for the mean and covariance of the PLS regression coefficients are available. They allow the construction of approximate confidence intervals and the application of test procedures (Kramer and Sugiyama 2012 <doi:10.1198/jasa.2011.tm10107>). Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available.

Last updated 2 years ago

3.67 score 3 stars 31 scripts 422 downloads

plsdepot - Partial Least Squares (PLS) Data Analysis Methods

Different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus.

Last updated 2 years ago

3.54 score 70 scripts 549 downloads

ModStatR - Statistical Modelling in Action with R

Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.

Last updated 9 months ago

3.40 score 5 stars 4 scripts 229 downloads

penalizedSVM - Feature Selection SVM using Penalty Functions

Support Vector Machine (SVM) classification with simultaneous feature selection using penalty functions is implemented. The smoothly clipped absolute deviation (SCAD), 'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic SCAD' (SCAD and 'L2-norm') penalties are available. The tuning parameters can be found using either a fixed grid or a interval search.

Last updated 2 years ago

3.33 score 1 stars 1 packages 72 scripts 330 downloads

CascadeData - Experimental Data of Cascade Experiments in Genomics

These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, 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>.

Last updated 2 years ago

2.70 score 1 stars 8 scripts 296 downloads

OneTwoSamples - Deal with One and Two (Normal) Samples

We introduce an R function one_two_sample() which can deal with one and two (normal) samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. For one normal sample x, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x. For two normal samples x and y, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x and y, respectively. It also reports interval estimation and test of hypothesis of mu1-mu2 (the difference of the means of x and y) and sigma1^2 / sigma2^2 (the ratio of the variances of x and y), tests whether x and y are from the same population, finds the correlation coefficient of x and y if x and y have the same length.

Last updated 2 years ago

2.43 score 27 scripts 378 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.

Last updated 2 years ago

2.40 score 25 scripts 266 downloads

granova - Graphical Analysis of Variance

This small collection of functions provides what we call elemental graphics for display of analysis of variance results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular analysis of variance methods. These functions can be particularly helpful for students and non-statistician analysts. But these methods should be quite generally helpful for work-a-day applications of all kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.

Last updated 2 years ago

1.54 score 35 scripts 307 downloads