Package 'Distr' Standardmethods Utility to Automatically Generate Accessor and Replacement Functions

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Package 'Distr' Standardmethods Utility to Automatically Generate Accessor and Replacement Functions

Package ‘distr’ March 11, 2019 Version 2.8.0 Date 2019-03-11 Title Object Oriented Implementation of Distributions Description S4-classes and methods for distributions. Depends R(>= 2.14.0), methods, graphics, startupmsg, sfsmisc Suggests distrEx, svUnit (>= 0.7-11), knitr Imports stats, grDevices, utils, MASS VignetteBuilder knitr ByteCompile yes Encoding latin1 License LGPL-3 URL http://distr.r-forge.r-project.org/ LastChangedDate {$LastChangedDate: 2019-03-11 16:33:22 +0100 (Mo, 11 Mrz 2019) $} LastChangedRevision {$LastChangedRevision: 1315 $} VCS/SVNRevision 1314 NeedsCompilation yes Author Florian Camphausen [ctb] (contributed as student in the initial phase --2005), Matthias Kohl [aut, cph], Peter Ruckdeschel [cre, cph], Thomas Stabla [ctb] (contributed as student in the initial phase --2005), R Core Team [ctb, cph] (for source file ks.c/ routines 'pKS2' and 'pKolmogorov2x') Maintainer Peter Ruckdeschel <[email protected]> Repository CRAN Date/Publication 2019-03-11 20:32:54 UTC 1 2 R topics documented: R topics documented: distr-package . .5 AbscontDistribution . 12 AbscontDistribution-class . 14 Arcsine-class . 18 Beta-class . 19 BetaParameter-class . 21 Binom-class . 22 BinomParameter-class . 24 Cauchy-class . 25 CauchyParameter-class . 27 Chisq-class . 28 ChisqParameter-class . 30 CompoundDistribution . 31 CompoundDistribution-class . 32 convpow-methods . 34 d-methods . 36 decomposePM-methods . 36 DExp-class . 37 df-methods . 39 df1-methods . 39 df2-methods . 40 dim-methods . 40 dimension-methods . 40 Dirac-class . 41 DiracParameter-class . 42 DiscreteDistribution . 44 DiscreteDistribution-class . 46 distr-defunct . 49 distrARITH . 50 Distribution-class . 51 DistributionSymmetry-class . 52 DistrList . 53 DistrList-class . 54 distrMASK . 55 distroptions . 56 DistrSymmList . 58 DistrSymmList-class . 59 EllipticalSymmetry . 59 EllipticalSymmetry-class . 60 EmpiricalDistribution . 61 EuclideanSpace-class . 62 Exp-class . 64 ExpParameter-class . 65 Fd-class . 67 flat.LCD . 69 flat.mix . 70 R topics documented: 3 FParameter-class . 71 Gammad-class . 72 GammaParameter-class . 74 gaps-methods . 75 Geom-class . 76 getLabel . 78 getLow,getUp . 79 Huberize-methods . 80 Hyper-class . 81 HyperParameter-class . 82 igamma . 84 img-methods . 84 k-methods . 85 lambda-methods . 85 Lattice-class . 86 LatticeDistribution . 87 LatticeDistribution-class . 89 Length-methods . 92 liesIn-methods . 93 liesInSupport . 93 Lnorm-class . 95 LnormParameter-class . 97 location-methods . 98 Logis-class . 99 LogisParameter-class . 101 m-methods . 102 makeAbscontDistribution . 102 Math-methods . 103 Max-methods . 104 mean-methods . 105 meanlog-methods . 105 Min-methods . 106 Minimum-methods . 106 n-methods . 108 name-methods . 108 Naturals-class . 109 Nbinom-class . 110 NbinomParameter-class . 112 ncp-methods . ..

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Relationships Among Some Univariate Distributions
IIE Transactions (2005) 37, 651–656 Copyright C “IIE” ISSN: 0740-817X print / 1545-8830 online DOI: 10.1080/07408170590948512 Relationships among some univariate distributions WHEYMING TINA SONG Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, Republic of China E-mail: [email protected], wheyming [email protected] Received March 2004 and accepted August 2004 The purpose of this paper is to graphically illustrate the parametric relationships between pairs of 35 univariate distribution families. The families are organized into a seven-by-five matrix and the relationships are illustrated by connecting related families with arrows. A simplified matrix, showing only 25 families, is designed for student use. These relationships provide rapid access to information that must otherwise be found from a time-consuming search of a large number of sources. Students, teachers, and practitioners who model random processes will find the relationships in this article useful and insightful. 1. Introduction 2. A seven-by-five matrix An understanding of probability concepts is necessary Figure 1 illustrates 35 univariate distributions in 35 if one is to gain insights into systems that can be rectangle-like entries. The row and column numbers are modeled as random processes. From an applications labeled on the left and top of Fig. 1, respectively. There are point of view, univariate probability distributions pro- 10 discrete distributions, shown in the first two rows, and vide an important foundation in probability theory since 25 continuous distributions. Five commonly used sampling they are the underpinnings of the most-used models in distributions are listed in the third row.
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Some Statistical Aspects of Spatial Distribution Models for Plants and Trees
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