This package creates interactive bubble chart visualizations for Shiny applications using the Nivo circle packing library. Provides an htmlwidgets wrapper around the Nivo circle packing chart, enabling hierarchical data visualization with customizable colors, labels, and interactive features including click and hover events. For more information about Nivo', see <https://nivo.rocks/>.

Data sets and nonlinear regression models dedicated to predictive microbiology.

Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting.

This package provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" <https://www.researchgate.net/publication/384354932_A_Novel_Nonvisual_Procedure_for_Screening_for_Nonstationarity_in_Time_Series_as_Obtained_from_Intensive_Longitudinal_Designs>.

In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.

This package provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the nimble modelling framework. It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.

This package implements the Network meta-Analytic Predictive (NAP) prior framework to accommodate changes in the standard of care (SoC) during ongoing randomized controlled trials (RCTs). The method synthesizes pre- and post-change in-trial data by leveraging external evidence, particularly head-to-head trials comparing the original and new standards of care, to bridge the two evidence periods and enable principled borrowing. The package provides utilities to construct NAP-based priors and perform Bayesian inference for time-to-event endpoints using summarized trial evidence.

Loading NONMEM (NONlinear Mixed-Effect Modeling, <https://www.iconplc.com/solutions/technologies/nonmem/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty.

This package implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardiaâ s Pareto of Type I, Logistic, Burr, Cook-Johnsonâ s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.

Robust nonparametric bootstrap and permutation tests for goodness of fit, distribution equivalence, location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the parallel package.

Non-negative Matrix Factorization.

Species Identification using DNA Barcodes Integrated with Environmental Niche Models.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

Set of functions to estimate kidney function and other traits of interest in nephrology.

Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more. NCutYX allows clustering of variables, of samples, and both variables and samples (biclustering), while incorporating the dependencies across multiple types of Omics data. (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).

Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.

This package performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable. The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.

Datasets of driving offences and fines in New Zealand between 2009 and 2017. Originally published by the New Zealand Police at <http://www.police.govt.nz/about-us/publication/road-policing-driver-offence-data-january-2009-december-2017>.

This package provides a collection of data structures that represent volumetric brain imaging data. The focus is on basic data handling for 3D and 4D neuroimaging data. In addition, there are function to read and write NIFTI files and limited support for reading AFNI files.

Analyzes data involving imprecise and vague information. Provides summary statistics and describes the characteristics of neutrosophic data, as defined by Florentin Smarandache (2013).<ISBN:9781599732749>.

Converts numeric vectors to character vectors of English number names. Provides conversion to cardinals, ordinals, numerators, and denominators. Supports negative and non-integer numbers.

Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.

This package provides functions for working with NHS number checksums. The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created.

The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.