This package implements the nonparametric trend test for one or several samples as proposed by Bathke (2009) <doi:10.1007/s00184-008-0171-x>. The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests.

The implementation of Markov Model Multiple Imputation with the application to COVID-19 scale, NIAID OS.

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>.

This package provides customized forest plots for network meta-analysis incorporating direct, indirect, and NMA effects. Includes visualizations of evidence contributions through proportion bars based on the hat matrix and evidence flow decomposition.

This package provides functions for reading cancer record files which follow a format defined by the North American Association of Central Cancer Registries (NAACCR).

Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. <doi:10.1109/TVCG.2017.2744018>.

Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.

Uses a modified lifting algorithm on which it builds the nondecimated lifting transform. It has applications in wavelet shrinkage.

Limpa e simplifica nomes de pessoas para auxiliar no pareamento de banco de dados na ausência de chaves únicas não ambà guas. Detecta e corrige erros tipográficos mais comuns, simplifica opcionalmente termos sujeitos eventualmente a omissão em cadastros, e simplifica foneticamente suas palavras, aplicando variação própria do algoritmo metaphoneBR. (Cleans and simplifies person names to assist in database matching when unambiguous unique keys are unavailable. Detects and corrects common typos, optionally simplifies terms prone to omission in records, and applies phonetic simplification using a custom variation of the metaphoneBR algorithm.) Mation (2025) <doi:10.6082/uchicago.15104>.

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

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>.

The NetCoupler algorithm identifies potential direct effects of correlated, high-dimensional variables formed as a network with an external variable. The external variable may act as the dependent/response variable or as an independent/predictor variable to the network.

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.

Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.

Common ecological distributions for nimble models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models. (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

This package provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.

This package provides tools for visualizing and analyzing the shape of discrete nominal frequency distributions. The package introduces centered frequency plots, in which nominal categories are ordered from the most frequent category at the center toward less frequent categories on both sides, facilitating the detection of distributional patterns such as uniformity, dominance, symmetry, skewness, and long-tail behavior. In addition, the package supports Pareto charts for the study of dominance and cumulative frequency structure in nominal data. The package is designed for exploratory data analysis and statistical teaching, offering visualizations that emphasize distributional form rather than arbitrary category ordering.

Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.

This package performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.

To add the table of numbers at risk below the Kaplan-Meier plot.

Semissupervised model for geographical document classification (Watanabe 2018) <doi:10.1080/21670811.2017.1293487>. This package currently contains seed dictionaries in English, German, French, Spanish, Italian, Russian, Hebrew, Arabic, Turkish, Japanese and Chinese (Simplified and Traditional).

Near-far matching is a study design technique for preprocessing observational data to mimic a pair-randomized trial. Individuals are matched to be near on measured confounders and far on levels of an instrumental variable. Methods outlined in further detail in Rigdon, Baiocchi, and Basu (2018) <doi:10.18637/jss.v086.c05>.

NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org> or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.

Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) <doi:10.1093/ije/dyaa031>.