mmcards - Playing Cards Utility Functions

Early insights in probability theory were largely influenced by questions about gambling and games of chance, as noted by Blitzstein and Hwang (2019, ISBN:978-1138369917). In modern times, playing cards continue to serve as an effective teaching tool for probability, statistics, and even 'R' programming, as demonstrated by Grolemund (2014, ISBN:978-1449359010). The 'mmcards' package offers a collection of utility functions designed to aid in the creation, manipulation, and utilization of playing card decks in multiple formats. These include a standard 52-card deck, as well as alternative decks such as decks defined by custom anonymous functions and custom interleaved decks. Optimized for the development of educational 'shiny' applications, the package is particularly useful for teaching statistics and probability through card-based games. Functions include shuffle_deck(), which creates either a shuffled standard deck or a shuffled custom alternative deck; deal_card(), which takes a deck and returns a list object containing both the dealt card and the updated deck; and i_deck(), which adds image paths to card objects, further enriching the package's utility in the development of interactive 'shiny' application card games.

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mmints - Workflows for Building Web Applications

Sharing statistical methods or simulation frameworks through 'shiny' applications often requires workflows for handling data. To help save and display simulation results, the postgresUI() and postgresServer() functions in 'mmints' help with persistent data storage using a 'PostgreSQL' database. The 'mmints' package also offers data upload functionality through the csvUploadUI() and csvUploadServer() functions which allow users to upload data, view variables and their types, and edit variable types before fitting statistical models within the 'shiny' application. These tools aim to enhance efficiency and user interaction in 'shiny' based statistical and simulation applications.

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bootwar - Nonparametric Bootstrap Test with Pooled Resampling Card Game

The card game War is simple in its rules but can be lengthy. In another domain, the nonparametric bootstrap test with pooled resampling (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, is optimal for comparing paired or unpaired means in non-normal data, especially for small sample size studies. However, many researchers are unfamiliar with these methods. The 'bootwar' package bridges this gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of the card game War designed for small samples. The package provides functions like score_keeper() and play_round() to streamline gameplay and scoring. Once a predetermined number of rounds concludes, users can employ the analyze_game() function to derive game results. This function leverages the 'npboottprm' package's nonparboot() to report nbpr results and, for comparative analysis, also reports results from the 'stats' package's t.test() function. Additionally, 'bootwar' features an interactive 'shiny' web application, bootwar(). This offers a user-centric interface to experience Boot War, enhancing understanding of nbpr methods across various distributions, sample sizes, number of bootstrap resamples, and confidence intervals.

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OLStrajr - Ordinary Least Squares Trajectory Analysis

The 'OLStrajr' package provides comprehensive functions for ordinary least squares (OLS) trajectory analysis and case-by-case OLS regression as outlined in Carrig, Wirth, and Curran (2004) <doi:10.1207/S15328007SEM1101_9> and Rogosa and Saner (1995) <doi:10.3102/10769986020002149>. It encompasses two primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies the estimation of individual growth curves over time via OLS regression, with options for visualizing both group-level and individual-level growth trajectories and support for linear and quadratic models. The cbc_lm() function facilitates case-by-case OLS estimates and provides unbiased mean population intercept and slope estimators by averaging OLS intercepts and slopes across cases. It further offers standard error calculations across bootstrap replicates and computation of 95% confidence intervals based on empirical distributions from the resampling processes.

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idmact - Interpreting Differences Between Mean ACT Scores

Interpreting the differences between mean scale scores across various forms of an assessment can be challenging. This difficulty arises from different mappings between raw scores and scale scores, complex mathematical relationships, adjustments based on judgmental procedures, and diverse equating functions applied to different assessment forms. An alternative method involves running simulations to explore the effect of incrementing raw scores on mean scale scores. The 'idmact' package provides an implementation of this approach based on the algorithm detailed in Schiel (1998) <https://www.act.org/content/dam/act/unsecured/documents/ACT_RR98-01.pdf> which was developed to help interpret differences between mean scale scores on the American College Testing (ACT) assessment. The function idmact_subj() within the package offers a framework for running simulations on subject-level scores. In contrast, the idmact_comp() function provides a framework for conducting simulations on composite scores.

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npboottprm - Nonparametric Bootstrap Test with Pooled Resampling

Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The 'npboottprm' package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, 'npboottprm' incorporates an interactive 'shiny' web application, nonparboot_app(), offering intuitive, user-friendly data exploration.

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scdtb - Single Case Design Tools

In some situations where researchers would like to demonstrate causal effects, it is hard to obtain a sample size that would allow for a well-powered randomized controlled trial. Single case designs are experimental designs that can be used to demonstrate causal effects with only one participant or with only a few participants. The 'scdtb' package provides a suite of tools for analyzing data from studies that use single case designs. The nap() function can be used to compute the nonoverlap of all pairs as outlined by the What Works Clearinghouse (2022) <https://ies.ed.gov/ncee/wwc/Handbooks>. The package also offers the mixed_model_analysis() and cross_lagged() functions which implement mixed effects models and cross lagged analyses as described in Maric & van der Werff (2020) <doi:10.4324/9780429273872-9>. The randomization_test() function implements randomization tests based on methods presented in Onghena (2020) <doi:10.4324/9780429273872-8>. The scdtb() 'shiny' application can be used to upload single case design data and access various 'scdtb' tools for plotting and analysis.

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npboottprmFBar - Informative Nonparametric Bootstrap Test with Pooled Resampling

Sample sizes are often small due to hard to reach target populations, rare target events, time constraints, limited budgets, or ethical considerations. Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, and informative hypothesis testing, which is implemented in the 'restriktor' package. The 'npboottprmFBar' package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power.

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mmiCATs - Cluster Adjusted t Statistic Applications

Simulation results detailed in Esarey and Menger (2019) <doi:10.1017/psrm.2017.42> demonstrate that cluster adjusted t statistics (CATs) are an effective method for correcting standard errors in scenarios with a small number of clusters. The 'mmiCATs' package offers a suite of tools for working with CATs. The mmiCATs() function initiates a 'shiny' web application, facilitating the analysis of data utilizing CATs, as implemented in the cluster.im.glm() function from the 'clusterSEs' package. Additionally, the pwr_func_lmer() function is designed to simplify the process of conducting simulations to compare mixed effects models with CATs models. For educational purposes, the CloseCATs() function launches a 'shiny' application card game, aimed at enhancing users' understanding of the conditions under which CATs should be preferred over random intercept models.

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holi - Higher Order Likelihood Inference Web Applications

Higher order likelihood inference is a promising approach for analyzing small sample size data. The 'holi' package provides web applications for higher order likelihood inference. It currently supports linear, logistic, and Poisson generalized linear models through the rstar_glm() function, based on Pierce and Bellio (2017) <doi:10.1111/insr.12232> and 'likelihoodAsy'. The package offers two main features: LA_rstar(), which launches an interactive 'shiny' application allowing users to fit models with rstar_glm() through their web browser, and sim_rstar_glm_pgsql(), which streamlines the process of launching a web-based 'shiny' simulation application that saves results to a user-created 'PostgreSQL' database.

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mmirestriktor - Informative Hypothesis Testing Web Applications

Offering enhanced statistical power compared to traditional hypothesis testing methods, informative hypothesis testing allows researchers to explicitly model their expectations regarding the relationships among parameters. An important software tool for this framework is 'restriktor'. The 'mmirestriktor' package provides 'shiny' web applications to implement some of the basic functionality of 'restriktor'. The mmirestriktor() function launches a 'shiny' application for fitting and analyzing models with constraints. The FbarCards() function launches a card game application which can help build intuition about informative hypothesis testing. The iht_interpreter() helps interpret informative hypothesis testing results based on guidelines in Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>.

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mmibain - Bayesian Informative Hypotheses Evaluation Web Applications

Researchers often have expectations about the relations between means of different groups or standardized regression coefficients; using informative hypothesis testing to incorporate these expectations into the analysis through order constraints increases statistical power Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>. Another valuable tool, the Bayes factor, can evaluate evidence for multiple hypotheses without concerns about multiple testing, and can be used in Bayesian updating Hoijtink, Mulder, van Lissa & Gu (2019) <doi:10.1037/met0000201>. The 'bain' R package enables informative hypothesis testing using the Bayes factor. The 'mmibain' package provides 'shiny' web applications based on 'bain'. The RepliCrisis() function launches a 'shiny' card game to simulate the evaluation of replication studies while the mmibain() function launches a 'shiny' application to fit Bayesian informative hypotheses evaluation models from 'bain'.

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exactamente - Explore the Exact Bootstrap Method

Researchers often use the bootstrap to understand a sample drawn from a population with unknown distribution. The exact bootstrap method is a practical tool for exploring the distribution of small sample size data. For a sample of size n, the exact bootstrap method generates the entire space of n to the power of n resamples and calculates all realizations of the selected statistic. The 'exactamente' package includes functions for implementing two bootstrap methods, the exact bootstrap and the regular bootstrap. The exact_bootstrap() function applies the exact bootstrap method following methodologies outlined in Kisielinska (2013) <doi:10.1007/s00180-012-0350-0>. The regular_bootstrap() function offers a more traditional bootstrap approach, where users can determine the number of resamples. The e_vs_r() function allows users to directly compare results from these bootstrap methods. To augment user experience, 'exactamente' includes the function exactamente_app() which launches an interactive 'shiny' web application. This application facilitates exploration and comparison of the bootstrap methods, providing options for modifying various parameters and visualizing results.

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swaprinc - Swap Principal Components into Regression Models

Obtaining accurate and stable estimates of regression coefficients can be challenging when the suggested statistical model has issues related to multicollinearity, convergence, or overfitting. One solution is to use principal component analysis (PCA) results in the regression, as discussed in Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package streamlines comparisons between a raw regression model with the full set of raw independent variables and a principal component regression model where principal components are estimated on a subset of the independent variables, then swapped into the regression model in place of those variables. The swaprinc() function compares one raw regression model to one principal component regression model, while the compswap() function compares one raw regression model to many principal component regression models. Package functions include parameters to center, scale, and undo centering and scaling, as described by Harvey and Hansen (2022) <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>. Additionally, the package supports using Gifi methods to extract principal components from categorical variables, as outlined by Rossiter (2021) <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.

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