R-universe search: exports:getX

Showing 12 of total 12 results (show query)

bioc

Rgraphviz:Provides plotting capabilities for R graph objects

Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package.

Maintained by Kasper Daniel Hansen. Last updated 7 days ago.

graphandnetwork visualization zlib

11.51 score 1.2k scripts 107 dependents

bioc

GWASTools:Tools for Genome Wide Association Studies

Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis.

Maintained by Stephanie M. Gogarten. Last updated 15 days ago.

snp geneticvariability qualitycontrol microarray

17 stars 10.67 score 396 scripts 5 dependents

bioc

oligo:Preprocessing tools for oligonucleotide arrays

A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).

Maintained by Benilton Carvalho. Last updated 25 days ago.

microarray onechannel twochannel preprocessing snp differentialexpression exonarray geneexpression dataimport zlib

3 stars 10.42 score 528 scripts 10 dependents

robinhankin

Brobdingnag:Very Large Numbers in R

Very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. Functionality for complex numbers is also provided. The package includes a vignette that gives a step-by-step introduction to using S4 methods.

Maintained by Robin K. S. Hankin. Last updated 7 months ago.

5 stars 9.92 score 77 scripts 70 dependents

boost-r

FDboost:Boosting Functional Regression Models

Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.

Maintained by David Ruegamer. Last updated 6 days ago.

boosting boosting-algorithms function-on-function-regression function-on-scalar-regression machine-learning scalar-on-function-regression variable-selection

19 stars 8.43 score 98 scripts

flr

FLa4a:A Simple and Robust Statistical Catch at Age Model

A simple and robust statistical Catch at Age model that is specifically designed for stocks with intermediate levels of data quantity and quality.

Maintained by Ernesto Jardim. Last updated 12 days ago.

12 stars 6.71 score 177 scripts 2 dependents

murphymv

semEff:Automatic Calculation of Effects for Piecewise Structural Equation Models

Automatically calculate direct, indirect, and total effects for piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). Confidence intervals are provided via bootstrapping.

Maintained by Mark V. Murphy. Last updated 7 months ago.

12 stars 6.20 score 29 scripts

clarahapp

funData:An S4 Class for Functional Data

S4 classes for univariate and multivariate functional data with utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description of the package functionalities and its interplay with the MFPCA package for multivariate functional principal component analysis <https://CRAN.R-project.org/package=MFPCA>.

Maintained by Clara Happ-Kurz. Last updated 1 years ago.

14 stars 6.15 score 111 scripts 6 dependents

mini-pw

nplr:N-Parameter Logistic Regression

Performing drug response analyses and IC50 estimations using n-Parameter logistic regression. Can also be applied to proliferation analyses.

Maintained by Tymoteusz Kwiecinski. Last updated 1 months ago.

6.02 score 39 scripts 3 dependents

glotaran

TIMP:Fitting Separable Nonlinear Models in Spectroscopy and Microscopy

A problem solving environment (PSE) for fitting separable nonlinear models to measurements arising in physics and chemistry experiments, as described by Mullen & van Stokkum (2007) <doi:10.18637/jss.v018.i03> for its use in fitting time resolved spectroscopy data, and as described by Laptenok et al. (2007) <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence Lifetime Imaging Microscopy (FLIM) data, in the study of Förster Resonance Energy Transfer (FRET). `TIMP` also serves as the computation backend for the `GloTarAn` software, a graphical user interface for the package, as described in Snellenburg et al. (2012) <doi:10.18637/jss.v049.i03>.

Maintained by Joris Snellenburg. Last updated 2 years ago.

parameter-estimation

3 stars 4.80 score 14 scripts 1 dependents

cran

CVST:Fast Cross-Validation via Sequential Testing

The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.

Maintained by Tammo Krueger. Last updated 3 years ago.

3.75 score 3 dependents

leahprice

ZVCV:Zero-Variance Control Variates

Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>), regularised ZV-CV (South et al., 2018 <arXiv:1811.05073>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semi-exact control functionals (SECF, South et al., 2020 <arXiv:2002.00033>). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.

Maintained by Leah F. South. Last updated 2 years ago.

openblas cpp

7 stars 3.54 score 5 scripts