* Some Movies to Illustrate Concepts in Statistics* (

**smovie**)

Provides movies to help students to understand statistical concepts. The ‘rpanel’ package <https://…/package=rpanel> is used to create interactive plots that move to illustrate key statistical ideas and methods. There are movies to: visualise probability distributions (including user-supplied ones); illustrate sampling distributions of the sample mean (central limit theorem), the sample maximum (extremal types theorem) and (the Fisher transformation of the) Pearson product moment correlation coefficient; examine the influence of an individual observation in simple linear regression; illustrate key concepts in statistical hypothesis testing. Also provided are dpqr functions for the distribution of the Fisher transformation of the correlation coefficient under sampling from a bivariate normal distribution.

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**Estimates the Win-Ratio as a Function of Time****winRatioAnalysis**)

Fits a model to data separately for each treatment group and then calculates the win-Ratio as a function of follow-up time.

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**Extracts Features from Text****textfeatures**)

A tool for extracting some generic features (e.g., number of words, line breaks, characters per word, URLs, lower case, upper case, commas, periods, exclamation points, etc.) from strings of text.

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**Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions****SimCorrMix**)

Generate continuous (normal, non-normal, or mixture distributions), binary, ordinal, and count (regular or zero-inflated, Poisson or Negative Binomial) variables with a specified correlation matrix, or one continuous variable with a mixture distribution. This package can be used to simulate data sets that mimic real-world clinical or genetic data sets (i.e., plasmodes, as in Vaughan et al., 2009 <DOI:10.1016/j.csda.2008.02.032>). The methods extend those found in the ‘SimMultiCorrData’ R package. Standard normal variables with an imposed intermediate correlation matrix are transformed to generate the desired distributions. Continuous variables are simulated using either Fleishman (1978)’s third order <DOI:10.1007/BF02293811> or Headrick (2002)’s fifth order <DOI:10.1016/S0167-9473(02)00072-5> polynomial transformation method (the power method transformation, PMT). Non-mixture distributions require the user to specify mean, variance, skewness, standardized kurtosis, and standardized fifth and sixth cumulants. Mixture distributions require these inputs for the component distributions plus the mixing probabilities. Simulation occurs at the component level for continuous mixture distributions. The target correlation matrix is specified in terms of correlations with components of continuous mixture variables. These components are transformed into the desired mixture variables using random multinomial variables based on the mixing probabilities. However, the package provides functions to approximate expected correlations with continuous mixture variables given target correlations with the components. Binary and ordinal variables are simulated using a modification of ordsample() in package ‘GenOrd’. Count variables are simulated using the inverse CDF method. There are two simulation pathways which calculate intermediate correlations involving count variables differently. Correlation Method 1 adapts Yahav and Shmueli’s 2012 method <DOI:10.1002/asmb.901> and performs best with large count variable means and positive correlations or small means and negative correlations. Correlation Method 2 adapts Barbiero and Ferrari’s 2015 modification of the ‘GenOrd’ package <DOI:10.1002/asmb.2072> and performs best under the opposite scenarios. The optional error loop may be used to improve the accuracy of the final correlation matrix. The package also contains functions to calculate the standardized cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables.

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**Bayesian Hierarchical Regression on Clearance Rates in the Presence of Lag and Tail Phases****bhrcr**)

An implementation of the Bayesian Clearance Estimator (Fogarty et al. (2015) <doi:10.1111/biom.12307>). It takes serial measurements of a response on an individual (e.g., parasite load after treatment) that is decaying over time and performs Bayesian hierarchical regression of the clearance rates on the given covariates. This package provides tools to calculate WWARN PCE (WorldWide Antimalarial Resistance Network’s Parasite Clearance Estimator) estimates of the clearance rates as well.