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r-nistunits
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Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for SI (International System of Units) and non-SI units, plus unit conversions Based on the data from NIST (National Institute of Standards and Technology, USA)
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2025-04-22 |
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r-nistnls
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Datasets for testing nonlinear regression routines.
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2025-04-22 |
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r-nipals
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Principal Components Analysis of a matrix using Non-linear Iterative Partial Least Squares 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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2025-04-22 |
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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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2025-04-22 |
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r-nightday
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Computes and plots the boundary between night and day.
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2025-04-22 |
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r-nicherover
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This package uses a probabilistic method to calculate niche regions and pairwise niche overlap 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. A forthcoming article by Swanson et al. (Ecology, 2014) provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data.
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2025-04-22 |
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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 currently contains a single simulated hospital dataset for teaching regression methods, with the addition of more datasets planned for future releases. This package exists to support skills development in the NHS-R community: <https://nhsrcommunity.com/>.
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2025-04-22 |
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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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2025-04-22 |
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r-nhds
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The National Hospital Discharge Survey (2010) summarizes the state of patients at the end of their hospital admissions. The US CDC publishes the data in the public domain, and describes it as follows: The National Hospital Discharge Survey (NHDS) is a continuing nationwide sample survey of short-stay hospitals in the United States. The scope of NHDS encompasses patients discharged from noninstitutional hospitals, exclusive of military and Department of Veterans Affairs hospitals, located in the 50 States and the District of Columbia. Only hospitals having six or more beds for in-patient use are included in the survey. See <https://www.cdc.gov/nchs/nhds> for more information.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-nfca
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Numerical Formal Concept Analysis (nFCA) is a modern unsupervised learning tool for analyzing general numerical data. Given input data, this R package nFCA outputs two nFCA graphs: a H-graph and an I-graph that reveal systematic, hierarchical clustering and inherent structure of the data.
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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 an interesting new alternative to economic time series filtering. The neverhpfilter package provides functions for implementing his solution. Hamilton (2017) <doi:10.3386/w23429>.
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2025-04-22 |
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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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2025-04-22 |
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r-neuroblastoma
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Annotated neuroblastoma copy number profiles, a benchmark data set for change-point detection algorithms.
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2025-04-22 |
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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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2025-04-22 |
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r-neural
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RBF and MLP neural networks with graphical user interface
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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.
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2025-04-22 |
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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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2025-04-22 |
