Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis. The R package pomp provides a very flexible framework for Monte Carlo statistical investigations using nonlinear, non-Gaussian POMP models. A range of modern statistical methods for POMP models have been implemented in this framework including sequential Monte Carlo, iterated filtering, particle Markov chain Monte Carlo, approximate Bayesian computation, maximum synthetic likelihood estimation, nonlinear forecasting, and trajectory matching. In this paper, we demonstrate the application of these methodologies using some simple toy problems. We also illustrate the specification of more complex POMP models, using a nonlinear epidemiological model with a discrete population, seasonality, and extra-demographic stochasticity. We discuss the specification of user-defined models and the development of additional methods within the programming environment provided by pomp. *This document is a version of a manuscript in press at the Journal of Statistical Software. It is provided under the Creative Commons Attribution License. Partially observed Markov processesapproximations are adequate for one's purposes, or when the latent process takes values in a small, discrete set, methods that exploit these additional assumptions to advantage, such as the extended and ensemble Kalman filter methods or exact hidden-Markov-model methods, are available, but not yet as part of pomp. It is the class of nonlinear, non-Gaussian POMP models with large state spaces upon which pomp is focused.A POMP model may be characterized by the transition density for the Markov process and the measurement density 1 . However, some methods require only simulation from the transition density whereas others require evaluation of this density. Still other methods may not work with the model itself but with an approximation, such as a linearization. Algorithms for which the dynamic model is specified only via a simulator are said to be plug-and-play (Bretó et al. 2009;He et al. 2010). Plug-and-play methods can be employed once one has "plugged" a model simulator into the inference machinery. Since many POMP models of scientific interest are relatively easy to simulate, the plug-and-play property facilitates data analysis. Even if one candidate model has tractable transition probabilities, a scientist will frequently wish to consider alternative models for which these probabilities are intractable. In a plug-andplay methodological environment, analysis of variations in the model can often be achieved by changing a few lines of the model simulator codes. The price one pays for the flexibility of plugand-play methodology is primarily additional computational effort, which can be substantial. Nevertheless, plug-and-play methods implemented using pomp have proved capable for state of the art inference problems (e.g., King et al. 2008;Bhadra et al. 2011;Shrestha et al. 2011Shrestha et al. , 2013Earn et al. 2012...

Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process. sequential Monte Carlo | particle filter | maximum likelihood | Markov processA n iterated filtering algorithm was originally proposed for maximum likelihood inference on partially observed Markov process (POMP) models by Ionides et al. (1). Variations on the original algorithm have been proposed to extend it to general latent variable models (2) and to improve numerical performance (3,4). In this paper, we study an iterated filtering algorithm that generalizes the data cloning method (5, 6) and is therefore also related to other Monte Carlo methods for likelihood-based inference (7-9). Data cloning methodology is based on the observation that iterating a Bayes map converges to a point mass at the maximum likelihood estimate. Combining such iterations with perturbations of model parameters improves the numerical stability of data cloning and provides a foundation for stable algorithms in which the Bayes map is numerically approximated by sequential Monte Carlo computations.We investigate convergence of a sequential Monte Carlo implementation of an iterated filtering algorithm that combines data cloning, in the sense of Lele et al. (5), with the stochastic parameter perturbations used by the iterated filtering algorithm of (1). Lindström et al. (4) proposed a similar algorithm, termed fast iterated filtering, but the theoretical support for that algorithm involved unproved conjectures. We present convergence results for our algorithm, which we call IF2. Empirically, it can dramatically outperform the previous iterated filtering algorithm of ref. 1, which we refer to as IF1. Although IF1 and IF2 both involve recursively filtering through the data, the theoretical justification and practical implementations of these algorithms are fundamentally different. IF1 approximates the Fisher score function, whereas IF2 implements an iterated Bayes map. IF1 has been used in applications for which no other computationally feasible algorithm for statistically efficient, likelihoodbased inference was known (10-15). The extra capabilities offered by IF2 open up further possibilities for drawing inferences about nonlinear partially observed stochastic dynamic models from time series data.Iterated filtering algorithms implemented using basic sequential Monte Carlo techniques have the property that they do not need to evaluate the transition density of the latent Markov process.Algorithms with this property...

Nguyen and Ehrenstein reveal that anti-TNF antibodies paradoxically enhance membrane TNF–TNF-RII interactions to increase Foxp3 expression and confer upon T reg cells the ability to suppress Th17 cells in rheumatoid arthritis patients.

Objective. The importance of interleukin-17 (IL-17

The biological target for interferon (IFN)-␣ in chronic myeloid leukemia (CML) is unknown, but one possibility is that amplification of granulocyte-macrophage colony-forming cells (CFU-GM) is reduced. Replating CFU-GM colonies and observing secondary colony formation provides a measure of CFU-GM amplification. Amplification of CML, but not normal, CFU-GM in vitro was significantly inhibited by IFN-␣ (