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r-contentid
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An interface for creating, registering, and resolving content-based identifiers for data management. Content-based identifiers rely on the 'cryptographic' hashes to refer to the files they identify, thus, anyone possessing the file can compute the identifier using a well-known standard algorithm, such as 'SHA256'. By registering a URL at which the content is accessible to a public archive (such as Hash Archive) or depositing data in a scientific repository such 'Zenodo', 'DataONE' or 'SoftwareHeritage', the content identifier can serve many functions typically associated with A Digital Object Identifier ('DOI'). Unlike location-based identifiers like 'DOIs', content-based identifiers permit the same content to be registered in many locations.
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2024-01-16 |
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r-convergenceconcepts
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This is a pedagogical package, designed to help students understanding convergence of random variables. It provides a way to investigate interactively various modes of convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d. random variables. Visualisation of simulated sample paths is possible through interactive plots. The approach is illustrated by examples and exercises through the function 'investigate', as described in Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>. The user can study his/her own sequences of random variables.
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2024-01-16 |
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r-convergenceclubs
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Functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).
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2024-01-16 |
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r-controltest
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Nonparametric two-sample procedure for comparing survival quantiles.
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2024-01-16 |
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r-contourfunctions
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Provides functions for making contour plots. The contour plot can be created from grid data, a function, or a data set. If non-grid data is given, then a Gaussian process is fit to the data and used to create the contour plot.
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2024-01-16 |
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r-connectapi
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Provides a helpful 'R6' class and methods for interacting with the 'Posit Connect' Server API along with some meaningful utility functions for regular tasks. API documentation varies by 'Posit Connect' installation and version, but the latest documentation is also hosted publicly at <https://docs.posit.co/connect/api/>.
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2024-01-16 |
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r-container
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Extends the functionality of base 'R' lists and provides specialized data structures 'deque', 'set', 'dict', and 'dict.table', the latter to extend the 'data.table' package.
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2024-01-16 |
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r-constellation
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Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.
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2024-01-16 |
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r-constants
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CODATA internationally recommended values of the fundamental physical constants, provided as symbols for direct use within the R language. Optionally, the values with uncertainties and/or units are also provided if the 'errors', 'units' and/or 'quantities' packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the "2018 CODATA" version, published on May 2019: Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2020) <https://physics.nist.gov/cuu/Constants/>.
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2024-01-16 |
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r-conspline
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Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline. Function outputs fits and various types of inference.
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2024-01-16 |
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r-conover.test
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Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.
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2024-01-16 |
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r-confintr
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Calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package 'boot'. Both one- and two-sided intervals are supported.
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2024-01-16 |
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r-connectednessapproach
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The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.
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2024-01-16 |
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r-connmattools
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Collects several different methods for analyzing and working with connectivity data in R. Though primarily oriented towards marine larval dispersal, many of the methods are general and useful for terrestrial systems as well.
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2024-01-16 |
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r-conjoint
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This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method.
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2024-01-16 |
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r-confsam
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For multiple testing. Computes estimates and confidence bounds for the False Discovery Proportion (FDP), the fraction of false positives among all rejected hypotheses. The methods in the package use permutations of the data. Doing so, they take into account the dependence structure in the data.
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2024-01-16 |
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r-confreq
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Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
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2024-01-16 |
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r-configr
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Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.
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2024-01-16 |
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r-conflicted
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R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use.
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2024-01-16 |
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r-configparser
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Enhances the 'ini' package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.
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2024-01-16 |
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r-confintvariance
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Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.
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2024-01-16 |
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r-condformat
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Apply and visualize conditional formatting to data frames in R. It renders a data frame with cells formatted according to criteria defined by rules, using a tidy evaluation syntax. The table is printed either opening a web browser or within the 'RStudio' viewer if available. The conditional formatting rules allow to highlight cells matching a condition or add a gradient background to a given column. This package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and exporting to an 'xlsx' file.
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2024-01-16 |
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r-confinterpret
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Produces descriptive interpretations of confidence intervals. Includes (extensible) support for various test types, specified as sets of interpretations dependent on where the lower and upper confidence limits sit. Provides plotting functions for graphical display of interpretations.
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2024-01-16 |
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r-concaveman
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The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.
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2024-01-16 |
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r-config
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Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value.
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2024-01-16 |
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r-conf.design
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This small library contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs.
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2024-01-16 |
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r-condvis
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Exploring fitted models by interactively taking 2-D and 3-D sections in data space.
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2024-01-16 |
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r-condmvnorm
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Computes conditional multivariate normal densities, probabilities, and random deviates.
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2024-01-16 |
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r-compositional
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Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. b) Tsagris M. (2014). "The k-NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3):519--534. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2):47--57. e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554>. g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. <doi:10.1111/anzs.12289>. j) Alenazi A. (2021). Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>. k) Alenazi, A. A. (2022). "f-divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. <doi:10.18187/pjsor.v18i4.3969>. l) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. <doi:10.1007/978-3-031-08329-7_10>. m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non-parametric regression models for compositional response data with zeros". Statistics and Computing, 33(5): 1--17. <doi:10.1007/s11222-023-10277-5>.
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2024-01-16 |
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r-conditionz
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Provides ability to control how many times in function calls conditions are thrown (shown to the user). Includes control of warnings and messages.
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2024-01-16 |
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r-concordance
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A set of utilities for matching products in different classification codes used in international trade research. It supports concordance between the Harmonized System (HS0, HS1, HS2, HS3, HS4, HS5, HS combined), the Standard International Trade Classification (SITC1, SITC2, SITC3, SITC4), the North American Industry Classification System (NAICS combined), as well as the Broad Economic Categories (BEC), the International Standard of Industrial Classification (ISIC), and the Standard Industrial Classification (SIC). It also provides code nomenclature/descriptions look-up, Rauch classification look-up (via concordance to SITC2), and trade elasticity look-up (via concordance to HS0 or SITC3 codes).
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2024-01-16 |
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r-conclust
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There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
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2024-01-16 |
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r-concatenate
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Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
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2024-01-16 |
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r-compute.es
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Several functions are available for calculating the most widely used effect sizes (ES), along with their variances, confidence intervals and p-values. The output includes ES's of d (mean difference), g (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and OR (odds ratio and log odds ratio). In addition, NNT (number needed to treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are computed. This package uses recommended formulas as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
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2024-01-16 |
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r-conake
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Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
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2024-01-16 |
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r-complexupset
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UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the 'UpSetR' and 'ggplot2', this package offers a way to create complex overlap visualisations, using simple and familiar tools, i.e. geoms of 'ggplot2'. For introduction to UpSet concept, see Lex et al. (2014) <doi:10.1109/TVCG.2014.2346248>.
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2024-01-16 |
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r-con2aqi
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To calculate the AQI (Air Quality Index) from pollutant concentration data. O3, PM2.5, PM10, CO, SO2, and NO2 are available currently. The method can be referenced at Environmental Protection Agency, United States as follows: EPA (2016) <https://www3.epa.gov/airnow/aqi-technical-assistance-document-may2016.pdf>.
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2024-01-16 |
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r-comprehenr
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Provides 'Python'-style list comprehensions. List comprehension expressions use usual loops (for(), while() and repeat()) and usual if() as list producers. In many cases it gives more concise notation than standard "*apply + filter" strategy.
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2024-01-16 |
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r-compr
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Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
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2024-01-16 |
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r-compound.cox
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Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023) <DOI:10.3390/biomedicines11030797>.
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2024-01-16 |
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r-completejourney
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Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files <https://www.8451.com/area51> which also includes useful metadata on products, coupons, campaigns, and promotions.
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2024-01-16 |
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r-comparedf
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Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changed in addition to summary statistics.
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2024-01-16 |
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r-comparegroups
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Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
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2024-01-16 |
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r-complexity
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Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.
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2024-01-16 |
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r-common
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Contains functions for solving commonly encountered problems while programming in R. This package is intended to provide a lightweight supplement to Base R, and will be useful for almost any R user.
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2024-01-16 |
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r-comparetests
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A standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.
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2024-01-16 |
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r-comparer
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Quickly run experiments to compare the run time and output of code blocks. The function mbc() can make fast comparisons of code, and will calculate statistics comparing the resulting outputs. It can be used to compare model fits to the same data or see which function runs faster. The R6 class ffexp$new() runs a function using all possible combinations of selected inputs. This is useful for comparing the effect of different parameter values. It can also run in parallel and automatically save intermediate results, which is very useful for long computations.
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2024-01-16 |
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r-compare
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Functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical.
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2024-01-16 |
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r-comorbidity
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Computing comorbidity indices and scores such as the weighted Charlson score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>) using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>). Australian and Swedish modifications of the Charlson Comorbidity Index are available as well (Sundararajan, 2004 <doi:10.1016/j.jclinepi.2004.03.012> and Ludvigsson, 2021 <doi:10.2147/CLEP.S282475>), together with different weighting algorithms for both the Charlson and Elixhauser comorbidity scores.
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2024-01-16 |
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r-commonsmath
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'Java' JAR files for the Apache Commons Mathematics Library for use by users and other packages.
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2024-01-16 |
