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r-aplore3
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An unofficial companion to "Applied Logistic Regression" by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.
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2024-01-16 |
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r-apa
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Formatter functions in the 'apa' package take the return value of a statistical test function, e.g. a call to chisq.test() and return a string formatted according to the guidelines of the APA (American Psychological Association).
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2024-01-16 |
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r-apercu
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The goal is to print an "aperçu", a short view of a vector, a matrix, a data.frame, a list or an array. By default, it prints the first 5 elements of each dimension. By default, the number of columns is equal to the number of lines. If you want to control the selection of the elements, you can pass a list, with each element being a vector giving the selection for each dimension.
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2024-01-16 |
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r-apdesign
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An implementation of the additive polynomial (AP) design matrix. It constructs and appends an AP design matrix to a data frame for use with longitudinal data subject to seasonality.
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2024-01-16 |
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r-apcanalysis
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Analysis of data from unreplicated orthogonal experiments
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2024-01-16 |
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r-anomalize
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The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.
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2024-01-16 |
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r-apc
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Functions for age-period-cohort analysis. Aggregate data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses. The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. Individual-level data should have a row for each individual and columns for each of age, period, and cohort. The statistical model for repeated cross-section is a generalized linear model. The statistical model for panel data is ordinary least squares. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification.
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2024-01-16 |
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r-apachelogprocessor
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Provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames.
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2024-01-16 |
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r-anesrake
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Provides a comprehensive system for selecting variables and weighting data to match the specifications of the American National Election Studies. The package includes methods for identifying discrepant variables, raking data, and assessing the effects of the raking algorithm. It also allows automated re-raking if target variables fall outside identified bounds and allows greater user specification than other available raking algorithms. A variety of simple weighted statistics that were previously in this package (version .55 and earlier) have been moved to the package 'weights.'
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2024-01-16 |
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r-aoos
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Another implementation of object-orientation in R. It provides syntactic sugar for the S4 class system and two alternative new implementations. One is an experimental version built around S4 and the other one makes it more convenient to work with lists as objects.
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2024-01-16 |
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r-aoptbdtvc
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A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.
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2024-01-16 |
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r-aods3
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Provides functions to analyse overdispersed counts or proportions. These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod.
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2024-01-16 |
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r-aod
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Provides a set of functions to analyse overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).
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2024-01-16 |
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r-antaresread
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Import, manipulate and explore results generated by 'Antares', a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about 'Antares' here : <https://antares-simulator.org/>).
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2024-01-16 |
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r-anylib
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Made to make your life simpler with packages, by installing and loading a list of packages, whether they are on CRAN, Bioconductor or github. For github, if you do not have the full path, with the maintainer name in it (e.g. "achateigner/topReviGO"), it will be able to load it but not to install it.
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2024-01-16 |
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r-antaresprocessing
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Process results generated by 'Antares', a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors.
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2024-01-16 |
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r-anocva
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Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering.
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2024-01-16 |
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r-antareseditobject
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Edit an 'Antares' simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update 'Antares' general & optimization settings. 'Antares' is an open source power system generator, more information available here : <https://antares-simulator.org/>.
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2024-01-16 |
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r-anndata
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A 'reticulate' wrapper for the Python package 'anndata'. Provides a scalable way of keeping track of data and learned annotations. Used to read from and write to the h5ad file format.
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2024-01-16 |
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r-andromeda
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Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.
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2024-01-16 |
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r-amt
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Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
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2024-01-16 |
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r-amr
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Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.
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2024-01-16 |
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r-andrews
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Visualisation of multidimensional data through different Andrews curves: Andrews, D. F. (1972) Plots of High-Dimensional Data. Biometrics, 28(1), 125-136. <doi:10.2307/2528964>.
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2024-01-16 |
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r-anapuce
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Functions for normalisation, differentially analysis of microarray data and local False Discovery Rate.
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2024-01-16 |
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r-ameshousing
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Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.
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2024-01-16 |
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r-analogsea
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Provides a set of functions for interacting with the 'DigitalOcean' API <https://www.digitalocean.com/>, including creating images, destroying them, rebooting, getting details on regions, and available images.
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2024-01-16 |
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r-alsm
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Functions and Data set presented in Applied Linear Statistical Models Fifth Edition (Chapters 1-9 and 16-25), Michael H. Kutner; Christopher J. Nachtsheim; John Neter; William Li, 2005. (ISBN-10: 0071122214, ISBN-13: 978-0071122214) that do not exist in R, are gathered in this package. The whole book will be covered in the next versions.
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2024-01-16 |
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r-ampd
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A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. For further information see <doi:10.3390/a5040588>.
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2024-01-16 |
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r-ammoniaconcentration
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Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.
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2024-01-16 |
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r-amerika
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A color palette generator inspired by American politics, with colors ranging from blue on the left to gray in the middle and red on the right. A variety of palettes allow for a range of applications from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015).
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2024-01-16 |
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r-amen
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Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). Several different link functions accommodate different relational data structures, including binary/network data, normal relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from fixed-rank nomination schemes. Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) <DOI:10.1017/nws.2013.17>. Development of this software was supported in part by NIH grant R01HD067509.
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2024-01-16 |
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r-amctestmaker
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Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.
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2024-01-16 |
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r-americancallopt
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This package includes a set of pricing functions for American call options. The following cases are covered: Pricing of an American call using the standard binomial approximation; Hedge parameters for an American call with a standard binomial tree; Binomial pricing of an American call with continuous payout from the underlying asset; Binomial pricing of an American call with an underlying stock that pays proportional dividends in discrete time; Pricing of an American call on futures using a binomial approximation; Pricing of a currency futures American call using a binomial approximation; Pricing of a perpetual American call. The user should kindly notice that this material is for educational purposes only. The codes are not optimized for computational efficiency as they are meant to represent standard cases of analytical and numerical solution.
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2024-01-16 |
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r-amcp
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Accompanies "Designing experiments and analyzing data: A model comparison perspective" (3rd ed.) by Maxwell, Delaney, & Kelley (2018; Routledge). Contains all of the data sets in the book's chapters and end-of-chapter exercises. Information about the book is available at <http://www.DesigningExperiments.com>.
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2024-01-16 |
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r-amelie
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Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.
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2024-01-16 |
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r-altopt
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Creates the optimal (D, U and I) designs for the accelerated life testing with right censoring or interval censoring. It uses generalized linear model (GLM) approach to derive the asymptotic variance-covariance matrix of regression coefficients. The failure time distribution is assumed to follow Weibull distribution with a known shape parameter and log-linear link functions are used to model the relationship between failure time parameters and stress variables. The acceleration model may have multiple stress factors, although most ALTs involve only two or less stress factors. ALTopt package also provides several plotting functions including contour plot, Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space (VDUS) plot. For more details, see Seo and Pan (2015) <doi:10.32614/RJ-2015-029>.
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2024-01-16 |
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r-alphahull
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Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.
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2024-01-16 |
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r-alphavantager
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Alpha Vantage has free historical financial information. All you need to do is get a free API key at <https://www.alphavantage.co>. Then you can use the R interface to retrieve free equity information. Refer to the Alpha Vantage website for more information.
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2024-01-16 |
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r-alr4
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Datasets to Accompany S. Weisberg (2014, ISBN: 978-1-118-38608-8), "Applied Linear Regression," 4th edition. Many data files in this package are included in the `alr3` package as well, so only one of them should be used.
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2024-01-16 |
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r-alscpc
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Using of the accelerated line search algorithm for simultaneously diagonalize a set of symmetric positive definite matrices.
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2024-01-16 |
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r-alluvial
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Creating alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data.
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2024-01-16 |
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r-aleplot
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Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.
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2024-01-16 |
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r-alleleretain
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Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations.
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2024-01-16 |
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r-allelematch
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Tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present; targeted for use with large datasets and databases containing multiple samples of each individual (a common situation in conservation genetics, particularly in non-invasive wildlife sampling applications). Functions explicitly incorporate missing data and can tolerate allele mismatches created by genotyping error. If you use this package, please cite the original publication in Molecular Ecology Resources (Galpern et al., 2012), the details for which can be generated using citation('allelematch'). For a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at <doi:10.1111/j.1755-0998.2012.03137.x>.
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2024-01-16 |
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r-allehap
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Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated.
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2024-01-16 |
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r-aiccmodavg
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Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
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2024-01-16 |
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r-algorithmia
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The company, Algorithmia, houses the largest marketplace of online algorithms. This package essentially holds a bunch of REST wrappers that make it very easy to call algorithms in the Algorithmia platform and access files and directories in the Algorithmia data API. To learn more about the services they offer and the algorithms in the platform visit <http://algorithmia.com>. More information for developers can be found at <https://algorithmia.com/developers>.
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2024-01-16 |
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r-algebraichaplopackage
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Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations.
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2024-01-16 |
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r-algaeclassify
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Functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 <doi:10.1111/fwb.12520>) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 1.3.0 and later no longer include the algae_search() function for querying the algaebase online taxonomic database (www.algaebase.org). Users are advised to verify taxonomic names directly using algaebase and cite the database in resulting publications. Note that none of the algaeClassify authors are affiliated with algaebase in any way. The algaeClassify package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the USGS John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). This software is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval by the U.S. Geological Survey (USGS). No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the software.
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2024-01-16 |
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r-alfr
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Allows you to connect to an 'Alfresco' content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the 'Alfresco' repository, read and upload content and manage folder hierarchies. For more details on the 'Alfresco' content management repository see <https://www.alfresco.com/ecm-software/document-management>.
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2024-01-16 |
