Nov 18, 2011 - are provided for: coercion, basic unary and binary operations on .... operations for the matrix.csr class. .... matical Software, 28, 301-324. He, X.
Randolph E. Bankâ . Craig C. Douglasâ¡. April 23, 2001. Abstract: Routines callable from FORTRAN and C are described which implement matrixâmatrix.
Journal of Statistical Software ... methods for Gaussian Markov random fields, but many other statistical applications are ... theory and application of GMRFs.
Jan 19, 2017 - sensing; generalized dissimilarity modelling; sparse canonical component analysis; R package. 1. Introduction. Global biodiversity change may ...
Sep 28, 2014 - Page 1 ... Trust region algorithms are nonlinear optimization tools that tend to be stable ... trustOptim: Sparse Trust Region Optimization in R.
May 17, 2012 - open source R-package called rCUR. This package will help users to ... In many modern data analysis applications, the user is faced with data ...
In this tutorial, we present the "Rattle" package which allows to the data miners ... To describe the use of the rattle
Aug 13, 2014 - dglars is a publicly available R package that implements the method ... In this paper we present the R (R Core Team 2014) dglars package ...
Aug 13, 2014 - Journal of Statistical Software. August 2014 ..... active set A and then, when the convergence is met, we
Aug 13, 2014 - Journal of Statistical Software. August 2014 .... At the MLE β = Ëβ, the Rao's score statistics are eq
S2 Inference score from CTD between Asthma and GALA genes . . 11 ..... Glutathione. T rinitrobenz enesulfonic Acid. Lipopolysaccharides. Metformin fulv estrant.
Jul 18, 2018 - Grant information: The funders ..... Thanks to Steph Locke (https://github.com/stephlocke) and Dave Parr (https://github.com/DaveRGP) from the.
Dec 10, 2008 - arXiv:0812.1869v1 [cs.LG] 10 Dec 2008. Convex Sparse Matrix Factorizations. Francis Bach, Julien Mairal, Jean Ponce. Willow Project-team.
CONTRIBUTED RESEARCH ARTICLES. asympTest: A Simple R Package for. Classical Parametric Statistical Tests and. Confidence Intervals in Large Samples.
The study included coached and uncoached pupils, about sixty in each of the ... (Buckingham, Clewley, Hutchinson, Sadler, and Shah 1997) produced by the ...
Feb 26, 2015 - 4 Basic Computations on and Characteristics of Fuzzy Numbers. 18 ...... ready) that to draw the membership function we do not need to ..... In this subsection we will use the following fuzzy number A for the sake of illustration:.
Apr 2, 2002 - The successful convergence of iterative methods may be dependent on ... The sparse storage scheme used in ITPACK is a common one. It is a ...
2 University of Minnesota, Duluth, MN 55812, USA, [email protected], http://www.d.umn.edu/Ëmasha. Abstract. This paper presents an overview of pARMS, ...
achieved using nonlinear least-squares algorithms [Dennis 1977], from which. Levenberg-Marquardt (LM) has proven to be of the most successful due to its.
User Guide for LDL, a concise sparse Cholesky package. Timothy A. Davis. â. VERSION 2.1.0, Jun 1, 2012. Abstract. The LDL software package is a set of short, ...
diagonal matrix D. Colon notation is used for submatrices. .... correct matrix is factorized, all entries of A should be provided when using the permutation input P.
els of performance in the presence of programming abstractions. In this paper we present an implementation of the Minimum Degree ordering al- gorithm using ...
2, and a 1.3 GHz IBM Power4. The machines are summarized in Table 1, where we show each processors' clock speed and cache configuration, along with.
description of the algorithm, it also helps reduce the memory requirements, which can be ... An index set is a sorted subset of the integers {1,2,...,n}. ... positions (15,9), (16,9), and (16,15) are nonzero. .... Adding the first columns of EJj Ii U
Mar 7, 2003 - SparseM can be obtained from the Comprehensive R Archive Network, CRAN, at http://cran.r-project.org/. 2 D
SparseM: A Sparse Matrix Package for R
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Roger Koenker and Pin Ng March 7, 2003
Abstract SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.
1
Introduction
Many applications in statistics involve large sparse matrices, matrices with a high proportion of zero entries. A typical example from parametric linear regression involves longitudinal \n") slm time = 11.88 0.53 12.44 0 0 > cat("lm time =", lm.time, "\n") lm time = 39.84 0 39.87 0 0 > cat("slm.fit time =", slm.fit.time, "\n") slm.fit time = 0.29 0.04 0.33 0 0 > cat("lm.fit time =", lm.fit.time, "\n") lm.fit time = 32.22 0 32.26 0 0 > cat("slm Results: Reported Coefficients Truncated to 5
", "\n")
slm Results: Reported Coefficients Truncated to 5 > sum.slm sum.slm$coef sum.slm Call: slm(formula = y ~ X1 - 1) Residuals: Min 1Q Median 3Q Max -1.952e-01 -1.400e-02 5.329e-15 1.442e-02 1.783e-01 Coefficients: Estimate Std. Error t value Pr(>|t|) [1,] 823.3613 0.1274 6460.4
