Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.

Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participantâ s reaction time (in milliseconds). A sample dataset (titled sampledata') is included in this package to practice with.

This package provides a multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. IMIX can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. IMIX uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. IMIX features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.

Estimates quantile-based inequality indicators from complex survey data, including the quantile ratio index (QRI), quintile share Ratio (QSR), Palma ratio, and percentile ratios, together with the Gini coefficient. Influence functions are provided for linearization and variance estimation, along with a rescaled bootstrap for complex sampling designs. Estimation from grouped data is also supported. See Scarpa et al. (2025) <doi:10.1093/jssam/smaf024> for details.

This package provides tools for passing messages between R processes. Shiny examples are provided showing how to perform useful tasks such as: updating reactive values from within a future, progress bars for long running async tasks, and interrupting async tasks based on user input.

An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.

Graphical visualization tools for analyzing the data produced by irace'. The iraceplot package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of irace and their performance. The functions just require the log file generated by irace and, in some cases, they can be used with user-provided data.

Automatically detects Copy Number Variations (CNV) from Next Generation Sequencing data using a machine learning algorithm, Isolation forest. More details about the method can be found in the paper by Cabello-Aguilar (2022) <doi:10.1101/2022.01.03.474771>.

This package provides a collection of Irucka Embry's miscellaneous USGS data sets (USGS Parameter codes with fixed values, USGS global time zone codes, and US Air Force Global Engineering Weather Data). Irucka created these data sets while a Cherokee Nation Technology Solutions (CNTS) United States Geological Survey (USGS) Contractor and/or USGS employee.

R is great for installing software. Through the installr package you can automate the updating of R (on Windows, using updateR()) and install new software. Software installation is initiated through a GUI (just run installr()), or through functions such as: install.Rtools(), install.pandoc(), install.git(), and many more. The updateR() command performs the following: finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation.

This package provides classes and functions for metrics calculation as part of the EarthScope MUSTANG project. The functionality in this package builds upon the base classes of the IRISSeismic package. Metrics include basic statistics as well as higher level health metrics that can help identify problematic seismometers.

Estimates the intraclass correlation coefficient for trajectory data using a matrix of distances between trajectories. The distances implemented are the extended Hausdorff distances (Min et al. 2007) <doi:10.1080/13658810601073315> and the discrete Fréchet distance (Magdy et al. 2015) <doi:10.1109/IntelCIS.2015.7397286>.

Offers a pipe-friendly alternative to the dplyr functions case_when() and if_else(), as well as a number of user-friendly simplifications for common use cases. These functions accept a vector as an optional first argument, allowing conditional statements to be built using the magrittr dot operator. The functions also coerce all outputs to the same type, meaning you no longer have to worry about using specific typed variants of NA or explicitly declaring integer outputs, and evaluate outputs somewhat lazily, so you don't waste time on long operations that won't be used.

Generates the equiplot, an iconic dot-plot graph for visualizing inequalities, as well as three complex inequality measures: the slope index of inequality, the concentration index and the mean absolute difference to the mean. For more details see World Health Organization (2013) <https://www.who.int/docs/default-source/gho-documents/health-equity/handbook-on-health-inequality-monitoring/handbook-on-health-inequality-monitoring.pdf>.

Extract and replace elements using indices that start from zero (rather than one), as is common in mathematical notation and other programming languages.

The digits of the old version (before 2000 year) of Chinese ID Card Number is 15, this package aims to update to the current version of 18 digits. Besides, this package can help check whether the given ID is right or not.

Up-to-date data from the Unicode CLDR Project (where CLDR stands for Common Locale Data Repository') are available here as a series of easy-to-parse datasets. Several functions are provided for extracting key elements from the tabular datasets.

Creation of tables of summary statistics or counts for clinical data (for TLFs'). These tables can be exported as in-text table (with the flextable package) for a Clinical Study Report (Word format) or a topline presentation (PowerPoint format), or as interactive table (with the DT package) to an html document for clinical data review.

Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See Cyprien Mbogning and Philippe Broet (2016)<doi:10.1186/s12859-016-1090-x> for an overview about the methods implemented in this package.

Characterisation and calibration of single or multiple Ion Selective Electrodes (ISEs); activity estimation of experimental samples. Implements methods described in: Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) <doi:10.1002/elan.201100510>, Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) <doi:10.1109/ICSENS.2017.8233898>, Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) <doi:10.3390/s19204544>, and Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020) <doi:10.1021/acssensors.9b02133>.

Run quality checks on data sets using the same checks that are conducted on the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk>.

An open source library for face detection in images. Provides a pretrained convolutional neural network based on <https://github.com/ShiqiYu/libfacedetection> which can be used to detect faces which have size greater than 10x10 pixels.

This package provides tools to scrape, clean, and analyze football player data from Indonesian leagues and perform similarity-based scouting analysis using standardized numeric features. The similarity approach follows common vector-space methods as described in Manning et al. (2008, ISBN:9780521865715) and Salton et al. (1975, <doi:10.1145/361219.361220>).

This package implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals from their single-nucleotide polymorphism (SNP) genotypes. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data.