Extends the classical Newman studentized range statistic in various ways that can be applied to genome-scale transcriptomic or other expression data.

Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.

This package implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

Apply neutrosophic regression type estimator and performs neutrosophic interval analysis including metric calculations for survey data.

Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).

Non-parametric dimensionality reduction function. Reduction with and without feature selection. Plot functions. Automated feature selections. Kosztyan et. al. (2024) <doi:10.1016/j.eswa.2023.121779>.

The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).

Simulate demand and attributes for ready to launch new products during their life cycle, or during their introduction and growth phases. You provide the number of products, attributes, time periods and/or other parameters and npdsim can simulate for you the demand for each product during the considered time periods, and the attributes of each product. The simulation for the demand is based on the idea that each product has a shape and a level, where the level is the cumulative demand over the considered time periods, and the shape is the normalized demand across those time periods.

Description: Provides methods for Neutrosophic Analysis of Variance (NANOVA) and Neutrosophic Analysis of Covariance (NANCOVA) for row-column designs, including Latin square designs and Youden square designs, using interval-valued observations. The package computes neutrosophic sums of squares, mean squares, interval-valued F-statistics, significance tests, and multiple comparisons using Least Significant Difference (LSD) procedures. For crisp data, users may enter identical lower and upper values of responses to obtain classical Analysis of Variance (ANOVA) results. Similarly, users may enter identical lower and upper values for both responses and covariates to obtain classical Analysis of Covariance (ANCOVA) results.

Package to select best model among several linear and nonlinear models. The main function uses the gnls() function from the nlme package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".

Runs several heterogeneity-robust difference-in-differences (DID) event-study estimators for non-absorbing (i.e., treatment can switch on and off over time, allowing treatment reversal) binary treatments through their respective packages, harmonizes their output onto a common time axis and tidy data structure, and overlays them in a single ggplot2 panel for visual comparison. Supported estimators include those provided by DIDmultiplegtDYN', PanelMatch', and fect', with an optional naive two-way fixed-effects reference series via fixest'. The underlying methods are respectively described in Clement de Chaisemartin and Xavier D'Haultfoeuille. "Difference-in-Differences Estimators of Intertemporal Treatment Effects." The Review of Economics and Statistics (2026) <doi:10.1162/rest_a_01414>, Kosuke Imai, In Song Kim, and Erik H. Wang. "Matching methods for causal inference with timeâ series crossâ sectional data." American Journal of Political Science 67.3 (2023) <doi:10.1111/ajps.12685>, Licheng Liu, Ye Wang, and Yiqing Xu. "A practical guide to counterfactual estimators for causal inference with timeâ series crossâ sectional data." American Journal of Political Science 68.1 (2024) <doi:10.1111/ajps.12723>, and Laurent R. Bergé, Kyle Butts, and Grant McDermott. "Fast and user-friendly econometrics estimations: The R package fixest'." arXiv preprint (2026) <doi:10.48550/arXiv.2601.21749>. A single nabs_event_study() wrapper runs any supported estimator with a common interface; nabs_event_study_simple() provides a one-line front door for quick exploratory runs; the S3 generic as_nabs_event_study() coerces estimator output into a tidy stable schema; and nabs_event_plot() overlays multiple methods on a single ggplot2 panel, with optional naive two-way fixed effects drawn in a neutral color as a reference.

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.

This package provides tools to create time series and geometry NetCDF files.

This package performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.

Non-negative Matrix Factorization.

This package performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.

NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. NACHO (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. NACHO does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.

This package provides tools for importing, harmonising, cleaning, analysing, benchmarking and visualising National Health Service (NHS) England Cancer Waiting Times data. The package supports national performance monitoring, provider-level benchmarking and cancer pathway comparisons through key performance indicator summaries, provider filtering, clustering analyses, mixed-effects regression models, variance decomposition, intraclass correlation coefficient estimation, adjusted provider performance estimation and sensitivity analyses. Functions are included for exploratory analysis, publication-ready visualisations and spreadsheet exports, supporting reproducible health services research, cancer services evaluation, quality improvement and assessment of waiting-time performance across healthcare organisations. Mixed-effects modelling functionality is based on Bates et al. (2015) <doi:10.18637/jss.v067.i01>. Multilevel modelling concepts and variance decomposition follow Gelman and Hill (2007, ISBN:9780521686891). Cancer Waiting Times definitions and reporting standards follow NHS England <https://www.england.nhs.uk/statistics/statistical-work-areas/cancer-waiting-times/>.

Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of JavaScript in one package.

Simplify the exploratory data analysis process for multiple network data sets with the help of hierarchical clustering, consensus clustering and heatmaps. Multiple network data consists of multiple disjoint networks that have common variables (e.g. ego networks). This package contains the necessary tools for exploring such data, from the data pre-processing stage to the creation of dynamic visualizations.

This package provides bridge equations with optional autoregressive terms for nowcasting low-frequency macroeconomic variables (e.g. quarterly GDP) from higher-frequency indicators (e.g. monthly retail sales). Handles the ragged-edge problem where different indicators have different publication lags via mixed-frequency alignment. Includes pseudo-real-time evaluation with expanding or rolling windows, and the Diebold-Mariano test for comparing forecast accuracy following Harvey, Leybourne, and Newbold (1997) <doi:10.1016/S0169-2070(96)00719-4>. No API calls; designed to work with data from any source.

This package performs network meta-analysis using integrated nested Laplace approximations ('INLA') which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. Includes methods to assess the heterogeneity and inconsistency in the network. Contains more than ten different network meta-analysis dataset. INLA package can be obtained from <https://www.r-inla.org>.

Statistical tool for learning the structure of direct associations among variables for continuous data, discrete data and mixed discrete-continuous data. The package is based on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.

This package provides a function designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations.