Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available: including: (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x> and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002> who use variables chosen by Roux and Mair (2010) <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute indices of racial or ethnic residential segregation, including but limited to those discussed in Massey & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of socioeconomic disparity.

Programmatically collect normalized news from (almost) any website. An R clone of the <https://github.com/kotartemiy/newscatcher> Python module.

This package provides a nomogram can not be easily applied, because it is difficult to calculate the points or even the survival probability. The package, including a function of nomogramEx(), is to extract the polynomial equations to calculate the points of each variable, and the survival probability corresponding to the total points.

For use in summary functions to omit missing values conditionally using specified checks.

Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.

Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.

This package performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). Logic and Methodology of Necessary but not Sufficient causality." Organizational Research Methods 19(1), 10-52) <doi:10.1177/1094428115584005>. NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line through the middle of the data in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations. (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is horizontal and the y-axis is vertical and that values increase upwards and to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output). A quick start guide for using this package can be found here: <https://repub.eur.nl/pub/78323/> or <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2624981>.

This package provides a suite of functions to work with data from the National Institutes of Health Brain Development Cohorts Data Hub. The package provides tools to create, clean, process, and filter datasets and associated metadata. These utilities are intended to simplify reproducible data-preparation for future research.

An interactive document on the topic of naive Bayes classification analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/NBShiny/>.

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

Designed to be compatible with the R package DBI (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this the R AWS Software Development Kit ('SDK') paws <https://github.com/paws-r/paws> is used as a driver.

Interface to the Nomis database (<https://www.nomisweb.co.uk>), maintained by Durham University on behalf of the Office for National Statistics (ONS). Provides access to UK labour market statistics including census data, benefit claimant counts, and employment surveys. Supports automatic pagination, optional disk caching, spatial data via sf', and tidy data output. Independent implementation unaffiliated with ONS or Durham University.

This package provides a nested menu widget for usage in Shiny applications. This is useful for hierarchical choices (e.g. continent, country, city).

Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum.

This package provides a flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the flipr package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" <doi:10.1111/rssc.12463>.

Draw nested extreme value random variables, which are the variables that appear in the latent variable formulation of the nested logit model.

This package implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

This package provides a comprehensive toolkit for analyzing and visualizing neural data outputs, including Principal Component Analysis (PCA) trajectory plotting, Multi-Electrode Array (MEA) heatmap generation, and variable importance analysis. Provides publication-ready visualizations with flexible customization options for neuroscience research applications.

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.

Free United Kingdom National Health Service (NHS) and other healthcare, or population health-related data for education and training purposes. This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data. This package exists to support skills development in the NHS-R community: <https://nhsrcommunity.com/>.

This package provides a flexible tool that can perform (i) traditional non-compartmental analysis (NCA) and (ii) Simulation-based posterior predictive checks for population pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics. The methods are described in Acharya et al. (2016) <doi:10.1016/j.cmpb.2016.01.013>.

Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.

Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.