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We discuss R package SQUAREM for accelerating iterative algorithms whichexhibit slow, monotone convergence. These include the well-knownexpectation-maximization algorithm, majorize-minimize (MM), and other EM-likealgorithms such as expectation conditional maximization, and generalized EMalgorithms. We demonstrate the simplicity, generality, and power of SQUAREMthrough a wide array of applications of EM/MM problems, including binaryPoisson mixture, factor analysis, interval censoring, genetics admixture, andlogistic regression maximum likelihood estimation (an MM problem). We show thatSQUAREM is easy to apply, and can accelerate any fixed-point, smooth,contraction mapping with linear convergence rate. Squared iterative scheme(Squarem) algorithm provides significant speed-up of EM-like algorithms. Themargin of the advantage for Squarem is especially huge for high-dimensionalproblems or when EM step is relatively time-consuming to evaluate. Squarem canbe used off-the-shelf since there is no need for the user to tweak any controlparameters to optimize performance. Given its remarkable ease of use, Squaremmay be considered as a default accelerator for slowly converging EM-likealgorithms. All the comparisons of CPU computing time in the paper are made ona quad-core 2.3 GHz Intel Core i7 Mac computer. R Package SQUAREM can bedownloaded at https://cran.r-project.org/web/packages/SQUAREM/index.html.

SQUAREM: An R Package for Off-the-Shelf Acceleration of EM, MM and Other EM-like Monotone Algorithms