MARSS is a package for fitting multivariate autoregressive state-space models to time-series data. The MARSS package implements state-space models in a maximum likelihood framework. The core functionality of MARSS is based on likelihood maximization using the Kalman filter/smoother, combined with an EM algorithm. To make comparisons with other packages available, parameter estimation is also permitted via direct search routines available in 'optim'. The MARSS package allows data to contain missing values and allows a wide variety of model structures and constraints to be specified (such as fixed or shared parameters). In addition to model-fitting, the package provides bootstrap routines for simulating data and generating confidence intervals, and multiple options for calculating model selection criteria (such as AIC).The MARSS package (Holmes et al., 2012) is an R package for fitting linear multivariate autoregressive state-space (MARSS) models with Gaussian errors to time-series data. This class of model is extremely important in the study of linear stochastic dynamical systems, and these models are used in many different fields, including economics, engineering, genetics, physics and ecology. The model class has different names in different fields; some common names are dynamic linear models (DLMs) and vector autoregressive (VAR) state-space models. There are a number of existing R packages for fitting this class of models, including sspir (Dethlefsen et al., 2009) for univariate data and dlm (Petris, 2010), dse (Gilbert, 2009), KFAS (Helske, 2011) and FKF (Luethi et al., 2012) for multivariate data. Additional packages are available on other platforms, such as SsfPack (Durbin and Koopman, 2001), EViews (www.eviews.com) and Brodgar (www.brodgar.com). Except for Brodgar and sspir, these packages provide maximization of the likelihood surface (for maximum-likelihood parameter estimation) via quasi-Newton or Nelder-Mead type algorithms. The MARSS package was developed to provide an alternative maximization algorithm, based instead on an Expectation-Maximization (EM) algorithm and to provide a standardized modelspecification framework for fitting different model structures.The MARSS package was originally developed for researchers analyzing data in the natural and environmental sciences, because many of the problems often encountered in these fields are not commonly encountered in disciplines like engineering or finance. Two typical problems are high fractions of irregularly spaced missing observations and observation error variance that cannot be estimated or known a priori (Schnute, 1994). Packages developed for other fields did not always allow estimation of the parameters of interest to ecologists because these parameters are always fixed in the package authors' field or application. The MARSS package was developed to address these issues and its three main differences are summarized as follows.First, maximum-likelihood optimization in most packages for fitting state-space models relies on quasi-Newton or Nelde...

The purpose of this article is to address a major gap in the instructional sensitivity literature on how to develop instructionally sensitive assessments. We propose an approach to developing and evaluating instructionally sensitive assessments in science and test this approach with one elementary lifescience module. The assessment we developed was administered to 125 students in seven classrooms. The development approach considered three dimensions of instructional sensitivity; that is, assessment items should: represent the curriculum content, reflect the quality of instruction, and have formative value for teaching. Focusing solely on the first dimension, representation of the curriculum content, this study was guided by the following research questions: (1) What science module characteristics can be systematically manipulated to develop items that prove to be instructionally sensitive? and (2) Are the instructionally sensitive assessments developed sufficiently valid to make inferences about the impact of instruction on students' performance? In this article, we describe our item development approach and provide empirical evidence to support validity arguments about the developed instructionally sensitive items. Results indicated that: (1) manipulations of the items at different proximities to vary their sensitivity were aligned with the rules for item development and also corresponded with pre-to-post gains; and (2) the items developed at different distances from the science module showed a pattern of pre-to-post gain consistent with their instructional sensitivity, that is, the closer the items were to the science module, the larger the observed gains and effect sizes. ß 2012 Wiley Periodicals, Inc. J Res Sci Teach 49: 2012

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Fitting a log binomial model to binary outcome data makes it possible to estimate risk and relative risk for follow-up data, and prevalence and prevalence ratios for cross-sectional data. However, the fitting algorithm may fail to converge when the maximum likelihood solution is on the boundary of the allowable parameter space. Some authorities recommend switching to Poisson regression with robust standard errors to approximate the coefficients of the log binomial model in those circumstances. This solves the problem of non-convergence, but results in errors in the coefficient estimates that may be substantial particularly when the maximum fitted value is large. The paradox is that the circumstances in which the modified Poisson approach is needed to overcome estimation problems are the same circumstances when the error in using it is greatest. We recommend that practitioners should be wary of using modified Poisson regression to approximate risk and relative risk.