Prepares data for statistical analysis (e.g., analysis of variance ;ANOVA) by enabling the user to easily and quickly merge (using the file_merge() function) raw data files into one merged table and then aggregate the merged table (using the prep() function) into a finalized table while keeping track and summarizing every step of the preparation. The finalized table contains several possibilities for dependent measures of the dependent variable. Most suitable when measuring variables in an interval or ratio scale (e.g., reaction-times) and/or discrete values such as accuracy. Main functions included are file_merge() and prep(). The file_merge() function vertically merges individual data files (in a long format) in which each line is a single observation to one single dataset. The prep() function aggregates the single dataset according to any combination of grouping variables (i.e., between-subjects and within-subjects independent variables, respectively), and returns a data frame with a number of dependent measures for further analysis for each cell according to the combination of provided grouping variables. Dependent measures for each cell include among others means before and after rejecting all values according to a flexible standard deviation criteria, number of rejected values according to the flexible standard deviation criteria, proportions of rejected values according to the flexible standard deviation criteria, number of values before rejection, means after rejecting values according to procedures described in Van Selst & Jolicoeur (1994; suitable when measuring reaction-times), standard deviations, medians, means according to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data frame prep() returns can also be exported as a txt file to be used for statistical analysis in other statistical programs.

This package provides methods for spatial predictive modeling, especially for spatial distribution models. This includes algorithms for model fitting and prediction, as well as methods for model evaluation.

This is an R package for pre-processing of flow and mass cytometry data. This package includes panel editing or renaming for FCS files, bead-based normalization and debarcoding.

This package provides a figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added (points, lines, barplot with add=TRUE and so forth).

Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.

This package implements the American Heart Association Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations from Khan SS, Matsushita K, Sang Y, and colleagues (2023) <doi:10.1161/CIRCULATIONAHA.123.067626>, with optional comparison with their de facto predecessor, the Pooled Cohort Equations from the American Heart Association and American College of Cardiology (2013) <doi:10.1161/01.cir.0000437741.48606.98> and the revision to the Pooled Cohort Equations from Yadlowsky and colleagues (2018) <doi:10.7326/M17-3011>.

Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.

Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.

An interactive document for preprocessing the dataset using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/PREPShiny/>.

Draw 2 dimensional and three dimensional plot for multiple regression models using package ggplot2 and rgl'. Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).

This package provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Enables researchers to visualize the prediction performance of any algorithm on the individual level (or close to it), given that the predicted outcome is either binary or continuous. Visual results are instantly comprehensible.

Perform a supervised data analysis on a database through a shiny graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.

Useful git hooks for R building on top of the multi-language framework pre-commit for hook management. This package provides git hooks for common tasks like formatting files with styler or spell checking as well as wrapper functions to access the pre-commit executable.

Compilation and digitalization of the official registry of victims of state terrorism in Argentina during the last military coup. The original data comes from RUVTE-ILID (2019) <https://www.argentina.gob.ar/sitiosdememoria/ruvte/informe> and <http://basededatos.parquedelamemoria.org.ar/registros/>. The title, presentes, comes from present in spanish.

This is a package for creating tiny yet beautiful documents and vignettes from R Markdown. The package provides the html_pretty output format as an alternative to the html_document and html_vignette engines that convert R Markdown into HTML pages. Various themes and syntax highlight styles are supported.

This package provides methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

This package provides functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting; <doi:10.1101/554725> , where methods are explained in detail.

Fetches the PREDICTS database and relevant metadata from the Data Portal at the Natural History Museum, London <https://data.nhm.ac.uk>. Data were collated from over 400 existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from sites around the world. These data are described in Hudson et al. (2013) <doi:10.1002/ece3.2579>.

One of the main advantages of using Generalised Linear Models is their interpretability. The goal of prettyglm is to provide a set of functions which easily create beautiful coefficient summaries which can readily be shared and explained. prettyglm helps users create coefficient summaries which include categorical base levels, variable importance and type III p.values. prettyglm also creates beautiful relativity plots for categorical, continuous and splined coefficients.

Consists of custom wrapper functions using packages openxlsx', flextable', and officer to create highly formatted MS office friendly output of your data frames. These viewer friendly outputs are intended to match expectations of professional looking presentations in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data frame to have certain properties in order to take advantage of the automated formatting.

Estimate sample size based on precision rather than power. precisely is a study planning tool to calculate sample size based on precision. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. precisely is based on the work of Rothman and Greenland (2018).

Recent years have seen an increased interest in novel methods for analyzing quantitative data from experimental psychology. Currently, however, they lack an established and accessible software framework. Many existing implementations provide no guidelines, consisting of small code snippets, or sets of packages. In addition, the use of existing packages often requires advanced programming experience. PredPsych is a user-friendly toolbox based on machine learning predictive algorithms. It comprises of multiple functionalities for multivariate analyses of quantitative behavioral data based on machine learning models.

This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.