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r-nipals
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Principal Components Analysis of a matrix using Non-linear Iterative Partial Least Squares or weighted Expectation Maximization PCA with Gram-Schmidt orthogonalization of the scores and loadings. Optimized for speed. See Andrecut (2009) <doi:10.1089/cmb.2008.0221>.
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2024-01-16 |
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r-nilde
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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.
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2024-01-16 |
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r-nightday
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Computes and plots the boundary between night and day.
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2024-01-16 |
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r-ngramrr
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A simple n-gram (contiguous sequences of n items from a given sequence of text) tokenizer to be used with the 'tm' package with no 'rJava'/'RWeka' dependency.
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2024-01-16 |
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r-nflseedr
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A set of functions to simulate National Football League seasons including the sophisticated tie-breaking procedures.
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2024-01-16 |
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r-nicherover
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Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. The article by Swanson et al. (2015) <doi:10.1890/14-0235.1> provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data.
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2024-01-16 |
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r-nhsrdatasets
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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/>.
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2024-01-16 |
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r-nhldata
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Each dataset contains scores for every game during a specific season of the NHL.
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2024-01-16 |
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r-nhanes
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Body Shape and related measurements from the US National Health and Nutrition Examination Survey (NHANES, 1999-2004). See http://www.cdc.gov/nchs/nhanes.htm for details.
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2024-01-16 |
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r-nflfastr
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A set of functions to access National Football League play-by-play data from <https://www.nfl.com/>.
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2024-01-16 |
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r-nflreadr
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A minimal package for downloading data from 'GitHub' repositories of the 'nflverse' project.
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2024-01-16 |
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r-nfwdist
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Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.
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2024-01-16 |
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r-neurobase
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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.
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2024-01-16 |
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r-nfl4th
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A set of functions to estimate outcomes of fourth down plays in the National Football League and obtain fourth down plays from <https://www.nfl.com/> and <https://www.espn.com/>.
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2024-01-16 |
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r-neverhpfilter
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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>.
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2024-01-16 |
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r-nfactors
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Indices, heuristics and strategies to help determine the number of factors/components to retain: 1. Acceleration factor (af with or without Parallel Analysis); 2. Optimal Coordinates (noc with or without Parallel Analysis); 3. Parallel analysis (components, factors and bootstrap); 4. lambda > mean(lambda) (Kaiser, CFA and related); 5. Cattell-Nelson-Gorsuch (CNG); 6. Zoski and Jurs multiple regression (b, t and p); 7. Zoski and Jurs standard error of the regression coeffcient (sescree); 8. Nelson R2; 9. Bartlett khi-2; 10. Anderson khi-2; 11. Lawley khi-2 and 12. Bentler-Yuan khi-2.
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2024-01-16 |
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r-newtestsurvrec
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Implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a model for recurrent event, when the data are correlated or not correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.
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2024-01-16 |
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r-newdistns
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Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
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2024-01-16 |
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r-neuralnettools
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public |
Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.
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2024-01-16 |
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r-neuroblastoma
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Annotated neuroblastoma copy number profiles, a benchmark data set for change-point detection algorithms, as described by Hocking et al. <doi:10.1186/1471-2105-14-164>.
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2024-01-16 |
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r-neurohcp
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Downloads and reads data from Human 'Connectome' Project <https://db.humanconnectome.org> using Amazon Web Services ('AWS') 'S3' buckets.
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2024-01-16 |
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r-networktools
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Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting.
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2024-01-16 |
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r-networklite
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An implementation of some of the core 'network' package functionality based on a simplified data structure that is faster in many research applications. This package is designed for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for binary and weighted, directed and undirected, bipartite and unipartite networks; no current support for multigraphs, hypergraphs, or loops.
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2024-01-16 |
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r-networktoolbox
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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.
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2024-01-16 |
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r-neuralnet
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Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.
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2024-01-16 |
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r-networkcomparisontest
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This permutation based hypothesis test, suited for several types of data supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance, several centrality measures, etc.). Network structures are estimated with l1-regularization. The Network Comparison Test is suited for comparison of independent (e.g., two different groups) and dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021), available from <doi:10.1037/met0000476>.
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2024-01-16 |
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r-netstat
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R interface for the 'netstat' command line utility used to retrieve and parse commonly used network statistics, including available and in-use transmission control protocol (TCP) ports. Primers offering technical background information on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" by Michael Kerrisk (2014) <https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) <https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.
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2024-01-16 |
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r-netmeta
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A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>.
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2024-01-16 |
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r-networkgen
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A network Maze generator that creates different types of network mazes.
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2024-01-16 |
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r-networkd3
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Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'.
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2024-01-16 |
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r-netweaver
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Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
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2024-01-16 |
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r-netswan
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A set of functions for studying network robustness, resilience and vulnerability.
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2024-01-16 |
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r-nestedmodels
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A modelling framework for nested data using the 'tidymodels' ecosystem. Specify how to nest data using the 'recipes' package, create testing and training splits using 'rsample', and fit models to this data using the 'parsnip' and 'workflows' packages. Allows any model to be fit to nested data.
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2024-01-16 |
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r-neonutilities
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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>.
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2024-01-16 |
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r-netsimr
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Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes a shiny tool to simulate insurance claims.
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2024-01-16 |
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r-netregr
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Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.
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2024-01-16 |
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r-netindices
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Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
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2024-01-16 |
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r-nestfs
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Implementation of forward selection based on cross-validated linear and logistic regression.
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2024-01-16 |
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r-nestedlogit
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Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663.
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2024-01-16 |
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r-nestedcv
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Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
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2024-01-16 |
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r-ncdfgeom
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Tools to create time series and geometry 'NetCDF' files.
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2024-01-16 |
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r-nephro
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Set of functions to estimate kidney function and other phenotypes of interest in nephrology based on different biomechimal traits.
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2024-01-16 |
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r-neighbr
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Classification, regression, and clustering with k nearest neighbors algorithm. Implements several distance and similarity measures, covering continuous and logical features. Outputs ranked neighbors. Most features of this package are directly based on the PMML specification for KNN.
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2024-01-16 |
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r-negbinbetabinreg
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The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.
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2024-01-16 |
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r-neat
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Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016, <DOI:10.1186/s12859-016-1203-6>).
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2024-01-16 |
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r-needs
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A simple function for easier package loading and auto-installation.
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2024-01-16 |
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r-ncopula
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Construct and manipulate hierarchical Archimedean copulas with multivariate compound distributions. The model used is the one of Cossette et al. (2017) <doi:10.1016/j.insmatheco.2017.06.001>.
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2024-01-16 |
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r-ndp
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An interactive presentation on the topic of normal distribution using 'rmarkdown' and 'shiny' packages. It is helpful to those who want to learn normal distribution quickly and get a hands on experience. The presentation has a template for solving problems on normal distribution. Runtime examples are provided in the package function as well as at <https://kartikeyastat.shinyapps.io/NormalDistribution/>.
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2024-01-16 |
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r-ncmisc
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A set of handy functions. Includes a versatile one line progress bar, one line function timer with detailed output, time delay function, text histogram, object preview, CRAN package search, simpler package installer, Linux command install check, a flexible Mode function, top function, simulation of correlated data, and more.
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2024-01-16 |
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r-ncbit
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Makes NCBI taxonomic data locally available and searchable as an R object.
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2024-01-16 |
