Library to plot performance profiles (Dolan and More (2002) <doi:10.1007/s101070100263>) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) <doi:10.19139/soic-2310-5070-679>) for a given data frame.

This package infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.

Due to Rstudio's status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with R'. The NMVANOVA (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice Rstudio users to perform different model variations one-way analysis of variance without downloading multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.

Non-linear shrinkage estimation of population eigenvalues and covariance matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).

Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.

Neural Additive Model framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.

This package provides a complete and seamless Nonmem simulation interface within R. Turns Nonmem control streams into simulation control streams, executes them with specified simulation input data and returns the results. The simulation is performed by Nonmem', eliminating manual work and risks of re-implementation of models in other tools.

Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) <doi:10.31234/osf.io/cz32t>.

This is the R API for the nfer formalism (<http://nfer.io/>). nfer was developed to specify event stream abstractions for spacecraft telemetry such as the Mars Science Laboratory. Users write rules using a syntax that borrows heavily from Allen's Temporal Logic that, when applied to an event stream, construct a hierarchy of temporal intervals with data. The R API supports loading rules from a file or mining them from historical data. Traces of events or pools of intervals are provided as data frames.

This package performs nonlinear Invariant Causal Prediction to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending Invariant Causal Prediction from Peters, Buehlmann and Meinshausen (2016), <arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: Invariant Causal Prediction for Nonlinear Models', <arXiv:1706.08576>.

This package provides a Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. NobBS learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2020) <doi:10.1371/journal.pcbi.1007735>.

Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are used, one based on the count matrix, and one using the alignment BAM files directly. Contains several options for every step of the process, as well as tools to quality check and assess the stability of output.

Represent network or igraph objects whose vertices can be represented by features in an sf object as a network graph surmising a sf plot. Fits into ggplot2 grammar.

Routines for enumerating all existing nonnegative integer solutions of a linear Diophantine equation. The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem.

This package provides a set of functions to visualize National Football League analysis in ggplot2 plots and gt tables.

The intent here is to enable the simulation of plays/drives and evaluate game-play strategies in the National Football League (NFL). Built-in strategies include going for it on fourth down and varying the proportion of passing/rushing plays during a drive. The user should be familiar with nflscrapR data before trying to write his/her own strategies. This work is inspired by a blog post by Mike Lopez, currently the Director of Data and Analytics at the NFL, Lopez (2019) <https://statsbylopez.netlify.app/post/resampling-nfl-drives/>.

The number of distinct alleles observed in a DNA mixture is informative of the number of contributors to the mixture. The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree.

Posterior distribution of case-control fine-mapping. Specifically, Bayesian variable selection for single-nucleotide polymorphism (SNP) data using the normal-gamma prior. Alenazi A.A., Cox A., Juarez M,. Lin W-Y. and Walters, K. (2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. <doi:10.1002/gepi.22213>.

Base package for Neuroconductor', which includes many helper functions that interact with objects of class nifti', implemented by package oro.nifti', for reading/writing and also other manipulation functions.

This package provides a tool set for food information and dietary assessment. It uses food composition data from several reference databases, including: USDA (United States), CIQUAL (France), BEDCA (Spain), CNF (Canada) and STFCJ (Japan). NutrienTrackeR calculates the intake levels for both macronutrient and micronutrients, and compares them with the recommended dietary allowances (RDA). It includes a number of visualization tools, such as time series plots of nutrient intake, and pie-charts showing the main foods contributing to the intake level of a given nutrient. A shiny app exposing the main functionalities of the package is also provided.

Simulate DNA sequences for the node substitution model. In the node substitution model, substitutions accumulate additionally during a speciation event, providing a potential mechanistic explanation for substitution rate variation. This package provides tools to simulate such a process, simulate a reference process with only substitutions along the branches, and provides tools to infer phylogenies from alignments. More information can be found in Janzen (2021) <doi:10.1093/sysbio/syab085>.

This package provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.

This package provides statistical methods for network meta-analysis of 1â 5 diagnostic tests to simultaneously compare multiple tests within a missing data framework, including: - Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests (Ma, Lian, Chu, Ibrahim, and Chen (2018) <doi:10.1093/biostatistics/kxx025>) - Bayesian Hierarchical Summary Receiver Operating Characteristic Model for Network Meta-Analysis of Diagnostic Tests (Lian, Hodges, and Chu (2019) <doi:10.1080/01621459.2018.1476239>).

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.