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r-nbddirichlet
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The Dirichlet (aka NBD-Dirichlet) model describes the purchase incidence and brand choice of consumer products. We estimate the model and summarize various theoretical quantities of interest to marketing researchers. Also provides functions for making tables that compare observed and theoretical statistics.
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2024-01-16 |
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r-nbconvertr
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Calls the 'Jupyter' script 'nbconvert' to create vignettes from notebooks. Those notebooks ('.ipynb' files) are files containing rich text, code, and its output. Code cells can be edited and evaluated interactively. See <https://jupyter.org/> for more information.
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2024-01-16 |
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r-nbclust
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It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
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2024-01-16 |
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r-nasapower
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An API client for NASA POWER global meteorology, surface solar energy and climatology data API. POWER (Prediction Of Worldwide Energy Resource) data are freely available for download with varying spatial resolutions dependent on the original data and with several temporal resolutions depending on the POWER parameter and community. This work is funded through the NASA Earth Science Directorate Applied Science Program. For more on the data themselves, the methodologies used in creating, a web- based data viewer and web access, please see <https://power.larc.nasa.gov/>.
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2024-01-16 |
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r-naturesounds
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Collection of example animal sounds for bioacoustic analysis.
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2024-01-16 |
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r-nbc4va
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An implementation of the Naive Bayes Classifier (NBC) algorithm used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) <DOI:10.1186/s12916-015-0521-2>.
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2024-01-16 |
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r-nb.mclust
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Model-based clustering of high-dimensional non-negative data that follow Generalized Negative Binomial distribution. All functions in this package applies to either continuous or integer data. Correlation between variables are allowed, while samples are assumed to be independent.
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2024-01-16 |
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r-naepirtparams
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This data package contains the Item Response Theory (IRT) parameters for the National Center for Education Statistics (NCES) items used on the National Assessment of Education Progress (NAEP) from 1990 to 2015. The values in these tables are used along with NAEP data to turn student item responses into scores and include information about item difficulty, discrimination, and guessing parameter for 3 parameter logit (3PL) items. Parameters for Generalized Partial Credit Model (GPCM) items are also included. The adjustments table contains the information regarding the treatment of items (e.g., deletion of an item or a collapsing of response categories), when these items did not appear to fit the item response models used to describe the NAEP data. Transformation constants change the score estimates that are obtained from the IRT scaling program to the NAEP reporting metric. Values from the years 2000 - 2013 were taken from the NCES website <https://nces.ed.gov/nationsreportcard/> and values from 1990 - 1998 and 2015 were extracted from their NAEP data files. All subtest names were reduced and homogenized to one word (e.g. "Reading to gain information" became "information"). The various subtest names for univariate transformation constants were all homogenized to "univariate".
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2024-01-16 |
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r-naturalsort
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Provides functions related to human natural ordering. It handles adjacent digits in a character sequence as a number so that natural sort function arranges a character vector by their numbers, not digit characters. It is typically seen when operating systems lists file names. For example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1 < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth characters.
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2024-01-16 |
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r-natserv
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Interface to 'NatureServe' (<https://www.natureserve.org/>). Includes methods to get data, image metadata, search taxonomic names, and make maps.
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2024-01-16 |
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r-nat.utils
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Utility functions that may be of general interest but are specifically required by the 'NeuroAnatomy Toolbox' ('nat'). Includes functions to provide a basic make style system to update files based on timestamp information, file locking and 'touch' utility. Convenience functions for working with file paths include 'abs2rel', 'split_path' and 'common_path'. Finally there are utility functions for working with 'zip' and 'gzip' files including integrity tests.
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2024-01-16 |
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r-nasaweather
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This package contains tidied data from the ASA 2006 data expo, as well as a number of useful other related data sets.
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2024-01-16 |
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r-nardl
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Computes the nonlinear cointegrating autoregressive distributed lag model with automatic bases aic and bic lags selection of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).
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2024-01-16 |
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r-mvoutlier
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Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.
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2024-01-16 |
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r-naptime
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Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error.
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2024-01-16 |
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r-nameneedle
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The Needleman-Wunsch global alignment algorithm can be used to find approximate matches between sample names in different data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.
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2024-01-16 |
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r-namedcapture
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User-friendly wrappers for named capture regular expressions. Introduction and comparison in research paper by Hocking (2019), R Journal. <doi:10.32614/RJ-2019-050> RE2 engine ('re2r' package) <https://github.com/qinwf/re2r> was removed from CRAN in Mar 2020 so must be installed from github.
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2024-01-16 |
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r-nakagami
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Density, distribution function, quantile function and random generation for the Nakagami distribution of Nakagami (1960) <doi:10.1016/B978-0-08-009306-2.50005-4>.
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2024-01-16 |
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r-naivebayes
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In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli, Categorical, Gaussian, Poisson and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.
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2024-01-16 |
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r-naepprimer
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Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
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2024-01-16 |
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r-mxm
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Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>. b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>. c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>. d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. <doi:10.1101/431734>. e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>. g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. <doi:10.1109/TCBB.2020.3029952>.
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2024-01-16 |
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r-mvquad
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Provides methods to construct multivariate grids, which can be used for multivariate quadrature. This grids can be based on different quadrature rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the multidimensional grid the product-rule or the combination- technique can be applied.
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2024-01-16 |
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r-nada
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Contains methods described by Dennis Helsel in his book "Nondetects And Data Analysis: Statistics for Censored Environmental Data".
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2024-01-16 |
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r-na.tools
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This comprehensive toolkit provide a consistent and extensible framework for working with missing values in vectors. The companion package 'tidyimpute' provides similar functionality for list-like and table-like structures). Functions exist for detection, removal, replacement, imputation, recollection, etc. of 'NAs'.
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2024-01-16 |
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r-mycor
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Perform correlation and linear regression test among the numeric fields in a data.frame automatically and make plots using pairs or lattice::parallelplot.
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2024-01-16 |
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r-mycobacrvr
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The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
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2024-01-16 |
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r-mwshiny
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A simple function, mwsApp(), that runs a 'shiny' app spanning multiple, connected windows. This uses all standard 'shiny' conventions, and depends only on the 'shiny' package.
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2024-01-16 |
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r-mxkssd
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Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, <doi:10.1080/15598608.2011.10483735>.
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2024-01-16 |
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r-mvtsplot
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A function for plotting multivariate time series data.
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2024-01-16 |
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r-mwright
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Implements random number generation, plotting, and estimation algorithms for the two-parameter one-sided and two-sided M-Wright (Mainardi-Wright) family. The M-Wright distributions naturally generalize the widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric (Airy and Gaussian or normal) models. These are widely studied in time-fractional differential equations. References: Cahoy and Minkabo (2017) <doi:10.3233/MAS-170388>; Cahoy (2012) <doi:10.1007/s00180-011-0269-x>; Cahoy (2012) <doi:10.1080/03610926.2010.543299>; Cahoy (2011); Mainardi, Mura, and Pagnini (2010) <doi:10.1155/2010/104505>.
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2024-01-16 |
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r-mvn
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Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests, and performs multivariate Box-Cox transformation (Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).
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2024-01-16 |
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r-mvtmeta
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Functions to run fixed effects or random effects multivariate meta-analysis.
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2024-01-16 |
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r-mvshapirotest
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This package implements the generalization of the Shapiro-Wilk test for multivariate normality proposed by Villasenor-Alva and Gonzalez-Estrada (2009).
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2024-01-16 |
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r-mvprobit
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Tools for estimating multivariate probit models, calculating conditional and unconditional expectations, and calculating marginal effects on conditional and unconditional expectations.
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2024-01-16 |
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r-mvinfluence
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Computes regression deletion diagnostics for multivariate linear models and provides some associated diagnostic plots. The diagnostic measures include hat-values (leverages), generalized Cook's distance, and generalized squared 'studentized' residuals. Several types of plots to detect influential observations are provided.
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2024-01-16 |
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r-mvntest
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Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test, Royston test and Henze-Zirkler test.
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2024-01-16 |
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r-mvnpermute
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Given a vector of multivariate normal data, a matrix of covariates and the data covariance matrix, generate new multivariate normal samples that have the same covariance matrix based on permutations of the transformed data residuals.
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2024-01-16 |
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r-mva
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Functions, data sets, analyses and examples from the book `An Introduction to Applied Multivariate Analysis with R' (Brian S. Everitt and Torsten Hothorn, Springer, 2011).
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2024-01-16 |
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r-mvnormtest
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Generalization of shapiro-wilk test for multivariate variables.
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2024-01-16 |
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r-mvnggrad
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Package for moving grid adjustment in plant breeding field trials.
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2024-01-16 |
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r-mvnbayesian
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Tools of Bayesian analysis framework using the method suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.
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2024-01-16 |
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r-mvmise
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Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness.
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2024-01-16 |
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r-mvmeta
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Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
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2024-01-16 |
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r-mvlm
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Allows a user to conduct multivariate multiple regression using analytic p-values rather than classic approximate F-tests.
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2024-01-16 |
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r-mvisage
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Pearson and Spearman correlation coefficients are commonly used to quantify the strength of bivariate associations of genomic variables. For example, correlations of gene-level DNA copy number and gene expression measurements may be used to assess the impact of DNA copy number changes on gene expression in tumor tissue. 'MVisAGe' enables users to quickly compute and visualize the correlations in order to assess the effect of regional genomic events such as changes in DNA copy number or DNA methylation level. Please see Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research <doi:10.1158/0008-5472.CAN-17-3464>.
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2024-01-16 |
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r-mvglmmrank
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Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>, Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.
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2024-01-16 |
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r-mvbutils
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Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.
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2024-01-16 |
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r-mvbinary
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Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
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2024-01-16 |
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r-music
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An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package.
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2024-01-16 |
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r-mus
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Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.
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2024-01-16 |
