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r-airr
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public |
Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community <http://docs.airr-community.org>.
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2024-01-16 |
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r-airgrteaching
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Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.
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2024-01-16 |
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r-ald
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It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.
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2024-01-16 |
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r-albopictus
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public |
Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) <doi:10.1371/journal.pone.0149282> and Erguler and others (2017) <doi:10.1371/journal.pone.0174293>.
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2024-01-16 |
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r-alabama
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public |
Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.
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2024-01-16 |
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r-ake
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public |
Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using associated kernels. The cross-validation technique and the local Bayesian procedure are also implemented for bandwidth selection.
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2024-01-16 |
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r-airports
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public |
Geographic, use, and property related data on airports.
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2024-01-16 |
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r-airgriwrm
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Semi-distributed Precipitation-Runoff Modelling based on 'airGR' package models integrating human infrastructures and their managements.
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2024-01-16 |
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r-aid
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public |
Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.
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2024-01-16 |
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r-agrmt
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Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes.
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2024-01-16 |
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r-aidar
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public |
Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R.
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2024-01-16 |
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r-agua
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public |
Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The package enables users to specify 'h2o' as an engine for several modeling methods.
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2024-01-16 |
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r-agror
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public |
Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).
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2024-01-16 |
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r-agricolae
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Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
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2024-01-16 |
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r-aggregater
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public |
Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed.
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2024-01-16 |
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r-agridat
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public |
Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.
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2024-01-16 |
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r-admiraldev
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public |
Utility functions to check data, variables and conditions for functions used in 'admiral' and 'admiral' extension packages. Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of 'admiral' and 'admiral' extension packages.
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2024-01-16 |
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r-agreementinterval
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A tool for calculating agreement interval of two measurement methods (Jason Liao (2015) <DOI:10.1515/ijb-2014-0030>) and present results in plots with discordance rate and/or clinically meaningful limit to quantify agreement quality.
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2024-01-16 |
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r-admiral
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public |
A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).
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2024-01-16 |
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r-aghmatrix
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Computation of A (pedigree), G (genomic-base), and H (A corrected by G) relationship matrices for diploid and autopolyploid species. Several methods are implemented considering additive and non-additive models.
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2024-01-16 |
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r-aggregation
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public |
Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)]. Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>].
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2024-01-16 |
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r-afpt
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Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) <doi:10.1098/rspa.2014.0952>. Taking inspiration from the program 'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. 'afpt' can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions.
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2024-01-16 |
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r-afex
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None |
Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).
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2024-01-16 |
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r-affluenceindex
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Enables to compute the statistical indices of affluence (richness) with bootstrap errors, and inequality and polarization indices. Moreover, gives the possibility of calculation of Medeiros affluence line. In 2.1 version some simple errors are fixed.
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2024-01-16 |
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r-aer
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None |
Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)
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2024-01-16 |
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r-afc
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This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009) <doi:10.1175/MWR-D-10-05069.1>.
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2024-01-16 |
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r-adwordsr
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Allows access to selected services that are part of the 'Google Adwords' API <https://developers.google.com/adwords/api/docs/guides/start>. 'Google Adwords' is an online advertising service by 'Google', that delivers Ads to users. This package offers a authentication process using 'OAUTH2'. Currently, there are two methods of data of accessing the API, depending on the type of request. One method uses 'SOAP' requests which require building an 'XML' structure and then sent to the API. These are used for the 'ManagedCustomerService' and the 'TargetingIdeaService'. The second method is by building 'AWQL' queries for the reporting side of the 'Google Adwords' API.
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2024-01-16 |
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r-advdif4
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This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t).
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2024-01-16 |
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r-adeptdata
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Created to host raw accelerometry data sets and their derivatives which are used in the corresponding 'adept' package.
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2024-01-16 |
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r-adpf
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This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information.
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2024-01-16 |
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r-adegraphics
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Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the 'ade4' package.
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2024-01-16 |
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r-adklakedata
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public |
Package for the access and distribution of Long-term lake datasets from lakes in the Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and biological parameters from 28 lakes. Data are from multiple collection organizations and have been harmonized in both time and space for ease of reuse.
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2024-01-16 |
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r-adgoftest
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None |
Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution"
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2024-01-16 |
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r-adfexplorer
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public |
Amiga Disk Files (ADF) are virtual representations of 3.5 inch floppy disks for the Commodore Amiga. Most disk drives from other systems (including modern drives) are not able to read these disks. To be able to emulate this system, the ADF format was created. This package enables you to read ADF files and import and export files from and to such virtual DOS-formatted disks.
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2024-01-16 |
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r-adequacymodel
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The main application concerns to a new robust optimization package with two major contributions. The first contribution refers to the assessment of the adequacy of probabilistic models through a combination of several statistics, which measure the relative quality of statistical models for a given data set. The second one provides a general purpose optimization method based on meta-heuristics functions for maximizing or minimizing an arbitrary objective function.
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2024-01-16 |
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r-addhaz
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Functions to fit the binomial and multinomial additive hazard models and to estimate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) and extended by Yokota et al (2017).
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2024-01-16 |
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r-additivitytests
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Implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell and modified Tukey non-additivity tests.
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2024-01-16 |
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r-ade4tkgui
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A Tcl/Tk GUI for some basic functions in the 'ade4' package.
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2024-01-16 |
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r-addt
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Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).
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2024-01-16 |
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r-activity
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Provides functions to express clock time data relative to anchor points (typically solar); fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; evaluate variation in linear variables with time (or other circular variables).
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2024-01-16 |
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r-adapttest
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The functions defined in this program serve for implementing adaptive two-stage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of two-stage tests, Statistica Sinica 2006.
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2024-01-16 |
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r-adabag
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It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function margins() is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as a function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies the relative importance measure to take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on unlabeled data. Version 4.2 includes the parallel computation option for some of the functions. Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandra and Plaia, 2023).
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2024-01-16 |
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r-adct
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public |
Existing adaptive design methods in clinical trials. The package includes power, stopping boundaries (sample size) calculation functions for two-group group sequential designs, adaptive design with coprimary endpoints, biomarker-informed adaptive design, etc.
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2024-01-16 |
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r-adaptmt
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Implementation of adaptive p-value thresholding (AdaPT), including both a framework that allows the user to specify any algorithm to learn local false discovery rate and a pool of convenient functions that implement specific algorithms. See Lei, Lihua and Fithian, William (2016) <arXiv:1609.06035>.
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2024-01-16 |
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r-activepathways
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Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The first version of the package is part of the following publication: Integrative Pathway Enrichment Analysis of Multivariate Omics Data. Paczkowska M^, Barenboim J^, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D, Mee MW, Boutros PC, PCAWG Drivers and Functional Interpretation Working Group; Reimand J, PCAWG Consortium. Nature Communications (2020) <doi:10.1038/s41467-019-13983-9>.
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2024-01-16 |
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r-adaptmcmc
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Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.
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2024-01-16 |
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r-adagio
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public |
The R package 'adagio' will provide methods and algorithms for discrete optimization, e.g. knapsack and subset sum procedures, derivative-free Nelder-Mead and Hooke-Jeeves minimization, and some (evolutionary) global optimization functions.
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2024-01-16 |
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r-activityindex
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public |
Reads raw 'accelerometry' from 'GT3X+' data and plain table data to calculate Activity Index from 'Bai et al.' (2016) <doi:10.1371/journal.pone.0160644>. The Activity Index refers to the square root of the second-level average variance of the three 'accelerometry' axes.
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2024-01-16 |
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r-ada
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public |
Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. The package ada provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets.
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2024-01-16 |
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r-activedriver
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A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 <doi:10.1038/msb.2012.68>.
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2024-01-16 |
