* Rolling Entry Matching* (

**rollmatch**)

Functions to perform propensity score matching on rolling entry interventions for which a suitable ‘entry’ date is not observed for nonparticipants. For more details, please reference Witman, Beadles, Hoerger, Liu, Kafali, Gandhi, Amico, and Larsen (2016) <https://…/9375>.

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**Generalized Pair Hidden Markov Chain Model for Sequence Alignment****gphmm**)

Implementation of a generalized pair hidden Markov chain model (GPHMM) that can be used to compute the probability of alignment between two sequences of nucleotides (e.g., a reference sequence and a noisy sequenced read). The model can be trained on a dataset where the noisy sequenced reads are known to have been sequenced from known reference sequences. If no training sets are available default parameters can be used.

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**Discrete Event Simulation****DES**)

Discrete event simulation (DES) involves modeling of systems having discrete, i.e. abrupt, state changes. For instance, when a job arrives to a queue, the queue length abruptly increases by 1. This package is an R implementation of the event-oriented approach to DES; see the tutorial in Matloff (2008) <http://…/DESimIntro.pdf>.

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**Simulate Temporally Autocorrelated Population Time Series****colorednoise**)

Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. The simulations are based on an assumption of an asexually reproducing population, but it can also be used to simulate females of a sexually reproducing species.

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**Soft Windowing on Linear Regression****SmoothWin**)

The main function in the package utilizes a windowing function in the form of an exponential weighting function. The bandwidth and sharpness of the window are controlled by two parameters. Then, a penalized change point detection is used to identify the right shape of the window (see Charles Kervrann (2004) <doi:10.1007/978-3-540-24672-5_11>).

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**Generalized Logistic Distribution****genlogis**)

Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006). It also has a interactive ‘RStudio’ plot for better guessing dynamically of initial values for ease of included optimization and simulating.