Abstract:

PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models. PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries NumPy and SciPy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimized and parallelized Fortran routines. These Fortran routines heavily utilize basic linear algebra and linear algebra Package functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

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Rating scales, such as Likert scales, are very common in marketing research, customer satisfaction studies, psychometrics, opinion surveys, population studies, and numerous other fields. We recommend diverging stacked bar charts as the primary graphical display technique for Likert and related scales. We also show other applications where diverging stacked bar charts are useful. Many examples of plots of Likert scales are given. We discuss the perceptual and programming issues in constructing these graphs. We present two implementations for diverging stacked bar charts. Most examples in this paper were drawn with the likert function included in the HH package in R. We also have a dashboard in Tableau.

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The YUIMA Project is an open source and collaborative effort aimed at developing the R package yuima for simulation and inference of stochastic differential equations. In the yuima package stochastic differential equations can be of very abstract type, multidimensional, driven by Wiener process or fractional Brownian motion with general Hurst parameter, with or without jumps specified as Lévy noise. The yuima package is intended to offer the basic infrastructure on which complex models and inference procedures can be built on. This paper explains the design of the yuima package and provides some examples of applications.

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We introduce growcurves for R that performs analysis of repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each subject through the subject’s participation in a set of multiple elements that characterize the intervention. In our motivating study design under which subjects receive a group cognitive behavioral therapy (CBT) treatment, an element is a group CBT session and each subject attends multiple sessions that, together, comprise the treatment. The sets of elements, or group CBT sessions, attended by subjects will partly overlap with some of those from other subjects to induce a dependence in their responses. The growcurves package offers two alternative sets of hierarchical models: 1. Separate terms are specified for multivariate subject and MM element random effects, where the subject effects are modeled under a Dirichlet process prior to produce a semi-parametric construction; 2. A single term is employed to model joint subject-by-MM effects. A fully non-parametric dependent Dirichlet process formulation allows exploration of differences in subject responses across different MM elements. This model allows for borrowing information among subjects who express similar longitudinal trajectories for flexible estimation. growcurves deploys “estimation” functions to perform posterior sampling under a suite of prior options. An accompanying set of “plot” functions allows the user to readily extract by-subject growth curves. The design approach intends to anticipate inferential goals with tools that fully extract information from repeated measures data. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++ code.

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The computer program DixonText.CriticalValues is written in VB.NET to extend the quadrature approach to calculate the critical values with accuracy up to 6 significant digits for Dixon’s ratios. Its use in creating the critical values tables in Excel is illustrated.

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This paper introduces the R package lavaan.survey, a user-friendly interface to design-based complex survey analysis of structural equation models (SEMs). By leveraging existing code in the lavaan and survey packages, the lavaan.survey package allows for SEM analyses of stratified, clustered, and weighted data, as well as multiply imputed complex survey data. lavaan.survey provides several features such as SEMs with replicate weights, a variety of resampling techniques for complex samples, and finite population corrections, features that should prove useful for SEM practitioners faced with the common situation of a sample that is not iid.

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We introduce here Momocs, a package intended to ease and popularize modern morphometrics with R, and particularly outline analysis, which aims to extract quantitative variables from shapes. It mostly hinges on the functions published in the book entitled Modern Morphometrics Using R by Claude (2008). From outline extraction from raw data to multivariate analysis, Momocs provides an integrated and convenient toolkit to students and researchers who are, or may become, interested in describing the shape and its variation. The methods implemented so far in Momocs are introduced through a simplistic case study that aims to test if two sets of bottles have different shapes.

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Quantifying non-linear dependence structures between two random variables is a challenging task. There exist several bona-fide dependence measures able to capture the strength of the non-linear association, but they typically give little information about how the variables are associated. This problem has been recognized by several authors and has given rise to the concept of local measures of dependence. A local measure of dependence is able to capture the “local” dependence structure in a particular region. The idea is that the global dependence structure is better described by a portfolio of local measures of dependence computed in different regions than a one-number measure of dependence. This paper introduces the R package localgauss which estimates and visualizes a measure of local dependence called local Gaussian correlation. The package provides a function for estimation, a function for local independence testing and corresponding functions for visualization purposes, which are all demonstrated with examples.

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This paper describes the core features of the R package mmeta, which implements the exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 × 2 table or multiple 2 × 2 tables when the risks within the same study are independent or correlated.

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The R package classify presents a number of useful functions which can be used to estimate the classification accuracy and consistency of assessments. Classification accuracy refers to the probability that an examinee’s achieved grade classification on an assessment reflects their true grade. Classification consistency refers to the probability that an examinee will be classified into the same grade classification under repeated administrations of an assessment. Understanding the classification accuracy and consistency of assessments is important where key decisions are being taken on the basis of grades or classifications. The study of classification accuracy can help to improve the design of assessments and aid public understanding and confidence in those assessments.

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The generalized order-restricted information criterion (GORIC) is a generalization of the Akaike information criterion such that it can evaluate hypotheses that take on specific, but widely applicable, forms (namely, closed convex cones) for multivariate normal linear models. It can examine the traditional hypotheses H0: β1,1 = … = βt,k and Hu: β1,1, …, βt,k and hypotheses containing simple order restrictions Hm: β1,1 ≥ … ≥ βt,k, where any "≥" may be replaced by "=" and m is the model/hypothesis index; with βh,j the parameter for the h-th dependent variable and the j-th predictor in a t-variate regression model with k predictors (which might include the intercept). But, the GORIC can also be applied to restrictions of the form Hm: R1β = r1; R2β ≥ r2, with β a vector of length tk, R1 a cm1 × tk matrix, r1 a vector of length cm1, R2 a cm2 × tk matrix, and r2 a vector of length cm2. It should be noted that [R1T, R2T]T should be of full rank when [R1T, R2T]T ≠ 0. In practice, this implies that one cannot examine range restrictions (e.g., 0 < β1,1 < 2 or β1,2 < β1,1 < 2β1,2) with the GORIC. A Fortran 90 program is presented, which enables researchers to compute the GORIC for hypotheses in the context of multivariate regression models. Additionally, an R package called goric is made by Daniel Gerhard and the first author.

]]>Dynamic Documents with R and knitr

Yihui Xie

Chapman & Hall/CRC, 2014

ISBN: 978-1-4822-0353-0