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Blind Source Separation Methods Based on Joint Diagonalization and Some BSS Performance Criteria
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017)
Population Genetic Data Analysis Using Genepop
Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Tools for Descriptive Statistics
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.
Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'
Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares
implementation utilizes the step-halving approach of Marschner (2011)
Computes the Variance-Covariance Matrix of Multidimensional Parameters Using M-Estimation
Provides a flexible framework for estimating the variance-covariance matrix of estimated parameters. Estimation relies on unbiased estimating functions to compute the empirical sandwich variance. (i.e., M-estimation in the vein of Tsiatis et al. (2019)
Sensory Data Analysis
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).
R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3)
Protocol Buffers are a way of encoding structured data in an
efficient yet extensible format. Google uses Protocol Buffers for almost all
of its internal 'RPC' protocols and file formats. Additional documentation
is available in two included vignettes one of which corresponds to our 'JSS'
paper (2016,
Multi-Fidelity Emulator for Computer Experiments with Tunable Fidelity Levels
Multi-Fidelity emulator for data from computer simulations of the
same underlying system but at different input locations and fidelity level,
where both the input locations and fidelity level can be continuous. Active
Learning can be performed with an implementation of the Integrated Mean Square
Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025,
Interface to the 'Open Tree of Life' API
An interface to the 'Open Tree of Life' API to retrieve phylogenetic trees, information about studies used to assemble the synthetic tree, and utilities to match taxonomic names to 'Open Tree identifiers'. The 'Open Tree of Life' aims at assembling a comprehensive phylogenetic tree for all named species.
'Rcpp' Bindings for the Boost Date_Time Library
Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using 'Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a 'ptime' object (but this needs recompilation with a #define set).