Introducing COZIGAM: An R Package for Unconstrained and Constrained Zero-Inflated Generalized Additive Model Analysis

Zero-inflation problem is very common in ecological studies as well as other areas. Nonparametric regression with zero-inflated data may be studied via the zero-inflated generalized additive model (ZIGAM), which assumes that the zero-inflated responses come from a probabilistic mixture of zero and a regular component whose distribution belongs to the 1-parameter exponential family. With the further assumption that the probability of non-zero-inflation is some monotonic function of the mean of the regular component, we propose the constrained zero-inflated generalized additive model (COZIGAM) for analyzingzero-inflated data. When the hypothesized constraint obtains, the new approach provides a unified framework for modeling zero-inflated data, which is more parsimonious and efficient than the unconstrained ZIGAM. We have developed an R package COZIGAM which contains functions that implement an iterative algorithm for fitting ZIGAMs and COZIGAMs to zero-inflated data basedon the penalized likelihood approach. Other functions included in the packageare useful for model prediction and model selection. We demonstrate the use ofthe COZIGAM package via some simulation studies and a real application.

PresenceAbsence: An R Package for Presence Absence Analysis

The PresenceAbsence package for R provides a set of functions useful when evaluating the results of presence-absence analysis, for example, models of species distribution or the analysis of diagnostic tests. The package provides a toolkit for selecting the optimal threshold for translating a probability surface into presence-absence maps specifically tailored to their intended use. The package includes functions for calculating threshold dependent measures such as confusion matrices, percent correctly classified (PCC), sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It also includes functions to plot the Receiver Operator Characteristic (ROC) curve and calculates the associated area under the curve (AUC), a threshold independent measure of model quality. Finally, the package computes optimal thresholds by multiple criteria, and plots these optimized thresholds on the graphs.

Interactive Data Visualization using Mondrian

This paper presents the Mondrian data visualization software. In addition to standard plots like histograms, barcharts, scatterplots or maps, Mondrian offers advanced plots for high dimensional categorical (mosaic plots) and continuous data (parallel coordinates). All plots are linked and offer various interaction techniques. A special focus is on the seamless integration of categorical data. Unique is Mondrian's special selection technique, which allows advanced selections in complex data sets. Besides loading data from local (ASCII) files it can connect to databases, avoiding a local copy of the data on the client machine. Mondrian is written in 100% pure JAVA.

Using R-based VOStat as a Low-Resolution Spectrum Analysis Tool

We describe here an online software suite VOStat written mainly for the Virtual Observatory, a novel structure in which astronomers share terabyte scale data. Written mostly in the public-domain statistical computing language and environment R, it can do a variety of statistical analysis on multidimensional, multi-epoch data with errors. Included are techniques which allow astronomers to start with multi-color data in the form of low-resolution spectra and select special kinds of sources in a variety of ways including color outliers. Here we describe the tool and demonstrate it with an example from Palomar-QUEST, a synoptic sky survey.

Computing the Two-Sided Kolmogorov-Smirnov Distribution

We propose an algorithm to compute the cumulative distribution function of the two-sided Kolmogorov-Smirnov test statistic D_n and its complementary distribution in a fast and reliable way. Different approximations are used in different regions of n, x. Java and C programs are available.

One-loop Neutron Electric Dipole Moment from Supersymmetry without R-parity

We present a detailed analysis together with exact numerical calculations on one-loop contributions to neutron electric dipole moment from supersymmetry without R-parity, focusing on the gluino, chargino, and neutralino contributions. Apart from the neglected family mixing among quarks, complete formulae are given for the various contributions, through the quark dipole operators, to which the present study is restricted. We discuss the structure and main features of the R-parity violating contributions and the interplay between the R-parity conserving and violating parameters. In particular, the parameter combination $\mu_i^*\lambda^{\prime}_{i11}$, under the optimal parametrization adopted, is shown to be solely responsible for the R-parity violating contributions in the supersymmetric loop diagrams. While $\mu_i^*\lambda^{\prime}_{i11}$ could bear a complex phase, the latter is not necessary to have a R-parity violating contribution.Comment: 43 pages Revtex with 15 eps- and 4 ps- figure files incoporated; proofread version to be published in Phys. Rev.

Exact Tests for Two-Way Contingency Tables with Structural Zeros

Fisher's exact test, named for Sir Ronald Aylmer Fisher, tests contingency tables for homogeneity of proportion. This paper discusses a generalization of Fisher's exact test for the case where some of the table entries are constrained to be zero. The resulting test is useful for assessing cases where the null hypothesis of conditional multinomial distribution is suspected to be false. The test is implemented in the form of a new R package, aylmer.

Bayesian Functional Data Analysis Using WinBUGS

We provide user friendly software for Bayesian analysis of functional data models using \pkg{WinBUGS}~1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.

A Computer Program to Calculate Two-Stage Short-Run Control Chart Factors for (X,MR) Charts

This paper is the second in a series of two papers that fully develops two-stage short-run (X, MR) control charts. This paper describes the development and execution of a computer program that accurately calculates first- and second-stage short-run control chart factors for (X, MR) charts using the equations derived in the first paper. The software used is Mathcad. The program accepts values for number of subgroups, alpha for the X chart, and alpha for the MR chart both above the upper control limit and below the lower control limit. Tables are generated for specific values of these inputs and the implications of the results are discussed. A numerical example illustrates the use of the program.

Mokken Scale Analysis in R

Mokken scale analysis (MSA) is a scaling procedure for both dichotomous and polytomous items. It consists of an item selection algorithm to partition a set of items into Mokken scales and several methods to check the assumptions of two nonparametric item response theory models: the monotone homogeneity model and the double monotonicity model. First, we present an R package mokken for MSA and explain the procedures. Second, we show how to perform MSA in R using test data obtained with the Adjective Checklist.