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Imputation of High-Dimensional Count Data using Side Information
Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019)
Simulate Data from a DAG and Associated Node Information
Simulate complex data from a given directed acyclic graph and information about each individual node. Root nodes are simply sampled from the specified distribution. Child Nodes are simulated according to one of many implemented regressions, such as logistic regression, linear regression, poisson regression and more. Also includes a comprehensive framework for discrete-time simulation, which can generate even more complex longitudinal data.
Variable Selection Using Random Forests
Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015) < https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.
The Hyperdirichlet Distribution, Mark 2
A suite of routines for the hyperdirichlet distribution
and reified Bradley-Terry; supersedes the 'hyperdirichlet' package;
uses 'disordR' discipline
Simultaneous Inference for Multiple Linear Contrasts in GEE Models
Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
Multivariate Polynomials with Rational Coefficients
Symbolic calculation and evaluation of multivariate
polynomials with rational coefficients. This package is strongly
inspired by the 'spray' package. It provides a function to
compute Gröbner bases (reference
Read and Write CSV on the Web (CSVW) Tables and Metadata
Provide functions for reading and writing CSVW - i.e. CSV tables and JSON metadata. The metadata helps interpret CSV by setting the types and variable names.
Estimate Parameters in the Generalized SBM
Given an adjacency matrix drawn from a Generalized Stochastic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019)
Discrete Multivariate Probability Distributions
Provides an object class for dealing with many multivariate probability distributions at once, useful for simulation.
Plot Functions for Use in Bibliometrics
Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see < http://crexplorer.net>). Further extension to more plot variants is planned.