This package contains utilities and functions for the cleaning, processing and management of patient level public health data for surveillance and analysis held by the UK Health Security Agency, UKHSA.

Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.

Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) <doi:10.1038/s41467-020-17851-9>; Kret & Sjak-Shie (2018) <doi:10.3758/s13428-018-1075-y>; Strauch (2024) <doi:10.1016/j.tins.2024.06.002>]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>]. eyeris provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) <doi:10.1038/s41592-018-0235-4>; Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44>]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology.

Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.

Simulates cyclic voltammetry, linear-sweep voltammetry (both with and without stirring of the solution), and single-pulse and double-pulse chronoamperometry and chronocoulometry experiments using the implicit finite difference method outlined in Gosser (1993, ISBN: 9781560810261) and in Brown (2015) <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide ways to display and to examine the results of these simulations. The primary purpose of this package is to provide tools for use in courses in analytical chemistry.

This package provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.

Simulating multi-arm cluster-randomized, multi-site, and simple randomized trials. Includes functions for conducting multilevel analyses using both Bayesian and Frequentist methods. Supports futility and superiority analyses through Bayesian approaches, along with visualization tools to aid interpretation and presentation of results.

An R interface to United States Environmental Protection Agency (EPA) Environmental Compliance History Online ('ECHO') Application Program Interface (API). ECHO provides information about EPA permitted facilities, discharges, and other reporting info associated with permitted entities. Data are obtained from <https://echo.epa.gov/>.

Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.

This package creates graphs of species associations (interactions) and ordination biplots from co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in Popovic, GC., Hui, FKC., Warton, DI., (2018) <doi:10.1016/j.jmva.2017.12.002>.

Illustrates the concepts developed in Sarkar and Rashid (2019, ISSN:0025-5742) <http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiH4deL3q3xAhWX73MBHR_wDaYQFnoECAUQAw&url=https%3A%2F%2Fwww.indianmathsociety.org.in%2Fmathstudent-part-2-2019.pdf&usg=AOvVaw3SY--3T6UAWUnH5-Nj6bSc>. This package helps a user guess four things (mean, MD, scaled MSD, and RMSD) before they get the SD. 1) The package displays the Empirical Cumulative Distribution Function (ECDF) of the given data. The user must choose the value of the mean by equating the areas of two colored (blue and green) regions. The package gives feedback to improve the choice until it is correct. Alternatively, the reader may continue with a different guess for the center (not necessarily the mean). 2) The user chooses the values of the Mean Deviation (MD) based on the ECDF of the deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 3) The user chooses the Scaled Mean Squared Deviation (MSD) based on the ECDF of the scaled square deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 4) The user chooses the Root Mean Squared Deviation (RMSD) by ensuring that its intersection with the ECDF of the deviations is at the same height as the intersection between the scaled MSD and the ECDF of the scaled squared deviations. Additionally, the intersection of two blue lines (the green dot) should fall on the vertical line at the maximum deviation. 5) Finally, if the mean is chosen correctly, only then the user can view the population SD (the same as the RMSD) and the sample SD (sqrt(n/(n-1))*RMSD) by clicking the respective buttons. If the mean is chosen incorrectly, the user is asked to correct it.

This package provides a toolbox for implementing the Ecological Dynamic Regime framework (Sánchez-Pinillos et al., 2023 <doi:10.1002/ecm.1589>) to characterize and compare groups of ecological trajectories in multidimensional spaces defined by state variables. The package includes the RETRA-EDR algorithm to identify representative trajectories, functions to generate, summarize, and visualize representative trajectories, and several metrics to quantify the distribution and heterogeneity of trajectories in an ecological dynamic regime and quantify the dissimilarity between two or more ecological dynamic regimes. The package also includes a set of functions to assess ecological resilience based on ecological dynamic regimes (Sánchez-Pinillos et al., 2024 <doi:10.1016/j.biocon.2023.110409>).

Process and analyze electronic health record (EHR) data. The EHR package provides modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional modules will be added in future. In addition, this package provides various functions useful to perform Phenome Wide Association Study (PheWAS) to explore associations between drug exposure and phenotypes obtained from EHR data, as outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) <doi:10.1093/bioinformatics/bty306>.

This package provides functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.

Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.

Instead of counting observations before and after a subset() call, the ExclusionTable() function reports the number before and after each subset() call together with the number of observations that have been excluded. This is especially useful in observational studies for keeping track how many observations have been excluded for each in-/ or exclusion criteria. You just need to provide ExclusionTable() with a dataset and a list of logical filter statements.

This package provides functions for the Bayesian analysis of extreme value models, using Markov chain Monte Carlo methods. Allows the construction of both uninformative and informed prior distributions for common statistical models applied to extreme event data, including the generalized extreme value distribution.

Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name "elitism" is an acronym of "e"quipment for "l"ogarithmic and l"i"near "ti"me "s"tepwise "m"ultiple hypothesis testing. See Gou, J. (2022), "Quick multiple test procedures and p-value adjustments", Statistics in Biopharmaceutical Research 14(4), 636-650.

This package provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.

Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.

This package creates text, LaTeX', Markdown, or Bootstrap-styled HTML-formatted odds ratio tables with confidence intervals for multiple logistic regression models.

This package provides a collection of functions for microbial ecology and other applications of genomics and metagenomics. Companion package for the Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016 <DOI:10.7287/peerj.preprints.1900v1>).

R shiny web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package epiworldR (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. epiworldRShiny provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.

Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.