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r-gender
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Infers state-recorded gender categories from first names and dates of birth using historical datasets. By using these datasets instead of lists of male and female names, this package is able to more accurately infer the gender of a name, and it is able to report the probability that a name was male or female. GUIDELINES: This method must be used cautiously and responsibly. Please be sure to see the guidelines and warnings about usage in the 'README' or the package documentation. See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.
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2024-01-16 |
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r-gentag
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Implement a coherent and flexible protocol for animal color tagging. 'GenTag' provides a simple computational routine with low CPU usage to create color sequences for animal tag. First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications.
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2024-01-16 |
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r-generalcorr
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Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.
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2024-01-16 |
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r-genord
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A gaussian copula based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.
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2024-01-16 |
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r-genoplotr
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public |
Draws gene or genome maps and comparisons between these, in a publication-grade manner. Starting from simple, common files, it will draw postscript or PDF files that can be sent as such to journals.
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2024-01-16 |
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r-genomicper
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Circular genomic permutation approach uses GWAS results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All SNPs in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values. The SNPs annotation and pathways annotations can be performed within the package or provided by the user.
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2024-01-16 |
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r-genmeta
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Generalized meta-analysis is a technique for estimating parameters associated with a multiple regression model through meta-analysis of studies which may have information only on partial sets of the regressors. It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) <doi:10.1093/biomet/asz030>.The current version (0.2.0) is updated to address some of the stability issues in the previous version (0.1).
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2024-01-16 |
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r-genomic.autocorr
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Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.
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2024-01-16 |
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r-geneticsubsetter
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Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
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2024-01-16 |
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r-genetics
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Classes and methods for handling genetic data. Includes classes to represent genotypes and haplotypes at single markers up to multiple markers on multiple chromosomes. Function include allele frequencies, flagging homo/heterozygotes, flagging carriers of certain alleles, estimating and testing for Hardy-Weinberg disequilibrium, estimating and testing for linkage disequilibrium, ...
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2024-01-16 |
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r-genesysr
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Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>). Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.
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2024-01-16 |
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r-generics
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In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.
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2024-01-16 |
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r-generator
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Allows users to quickly and easily generate fake data containing Personally Identifiable Information (PII) through convenience functions.
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2024-01-16 |
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r-generalizedhyperbolic
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Functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data. Linear models with hyperbolic errors may be fitted using hyperblmFit.
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2024-01-16 |
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r-generaloaxaca
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Perform the Blinder-Oaxaca decomposition for generalized linear model with bootstrapped standard errors. The twofold and threefold decomposition are given, even the generalized linear model output in each group.
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2024-01-16 |
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r-gdalutilities
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R's 'sf' package ships with self-contained 'GDAL' executables, including a bare bones interface to several 'GDAL'-related utility programs collectively known as the 'GDAL utilities'. For each of those utilities, this package provides an R wrapper whose formal arguments closely mirror those of the 'GDAL' command line interface. The utilities operate on data stored in files and typically write their output to other files. Therefore, to process data stored in any of R's more common spatial formats (i.e. those supported by the 'sf' and 'terra' packages), first write them to disk, then process them with the package's wrapper functions before reading the outputted results back into R. GDAL function arguments introduced in GDAL version 3.5.2 or earlier are supported.
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2024-01-16 |
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r-generalhoslem
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Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal).
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2024-01-16 |
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r-genemodel
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Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.
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2024-01-16 |
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r-genef
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Implementation of several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing. Based on: Y. Lai (2011) <doi:10.1371/journal.pone.0019754>.
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2024-01-16 |
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r-gdatools
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Many tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>), such as MCA variants (Specific Multiple Correspondence Analysis, Class Specific Analysis), many graphical and statistical aids to interpretation (structuring factors, concentration ellipses, inductive tests, bootstrap validation, etc.) and multiple-table analysis (Multiple Factor Analysis, between- and inter-class analysis, Principal Component Analysis and Correspondence Analysis with Instrumental Variables, etc.).
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2024-01-16 |
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r-gendist
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Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
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2024-01-16 |
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r-gendata
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Set of functions to create datasets using a correlation matrix.
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2024-01-16 |
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r-genbinomapps
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Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
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2024-01-16 |
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r-genalg
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R based genetic algorithm for binary and floating point chromosomes.
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2024-01-16 |
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r-geem
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GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting. The 'M' in the name 'geeM' is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R.
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2024-01-16 |
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r-gen2stage
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One can find single-stage and two-stage designs for a phase II single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>.
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2024-01-16 |
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r-gdistance
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Provides classes and functions to calculate various distance measures and routes in heterogeneous geographic spaces represented as grids. The package implements measures to model dispersal histories first presented by van Etten and Hijmans (2010) <doi:10.1371/journal.pone.0012060>. Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. The distances implemented in the package are used in geographical genetics, accessibility indicators, and may also have applications in other fields of geospatial analysis.
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2024-01-16 |
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r-gds
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Contains a function called gds() which accepts three input parameters like lower limits, upper limits and the frequencies of the corresponding classes. The gds() function calculate and return the values of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'), inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate effective data analysis. For skewness and kurtosis calculations we use moments.
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2024-01-16 |
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r-gdata
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Various R programming tools for data manipulation, including medical unit conversions, combining objects, character vector operations, factor manipulation, obtaining information about R objects, generating fixed-width format files, extracting components of date & time objects, operations on columns of data frames, matrix operations, operations on vectors, operations on data frames, value of last evaluated expression, and a resample() wrapper for sample() that ensures consistent behavior for both scalar and vector arguments.
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2024-01-16 |
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r-gb2
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Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.
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2024-01-16 |
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r-gd
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Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.
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2024-01-16 |
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r-gbutils
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Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.
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2024-01-16 |
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r-gdadata
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Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
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2024-01-16 |
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r-gamlss.cens
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This is an add-on package to GAMLSS. The purpose of this package is to allow users to fit interval response variables in GAMLSS models. The main function gen.cens() generates a censored version of an existing GAMLSS family distribution.
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2024-01-16 |
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r-gcookbook
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Data sets used in the book "R Graphics Cookbook" by Winston Chang, published by O'Reilly Media.
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2024-01-16 |
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r-gclus
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Orders panels in scatterplot matrices and parallel coordinate displays by some merit index. Package contains various indices of merit, ordering functions, and enhanced versions of pairs and parcoord which color panels according to their merit level.
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2024-01-16 |
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r-gcd
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Contains the Global Charcoal database data. Data include charcoal series (age, depth, charcoal quantity, associated units and methods) and information on sedimentary sites (localisation, depositional environment, biome, etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.
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2024-01-16 |
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r-gapminder
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An excerpt of the data available at Gapminder.org. For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007.
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2024-01-16 |
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r-gbrd
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Provides utilities for processing Rd objects and files. Extract argument descriptions and other parts of the help pages of functions.
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2024-01-16 |
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r-gaussfacts
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Display a random fact about Carl Friedrich Gauss based the on collection curated by Mike Cavers via the <http://gaussfacts.com> site.
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2024-01-16 |
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r-gaussdiff
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A collection difference measures for multivariate Gaussian probability density functions, such as the Euclidea mean, the Mahalanobis distance, the Kullback-Leibler divergence, the J-Coefficient, the Minkowski L2-distance, the Chi-square divergence and the Hellinger Coefficient.
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2024-01-16 |
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r-gatepoints
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Allows user to choose/gate a region on the plot and returns points within it.
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2024-01-16 |
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r-gargle
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Provides utilities for working with Google APIs <https://developers.google.com/apis-explorer>. This includes functions and classes for handling common credential types and for preparing, executing, and processing HTTP requests.
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2024-01-16 |
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r-garray
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Organize a so-called ragged array as generalized arrays, which is simply an array with sub-dimensions denoting the subdivision of dimensions (grouping of members within dimensions). By the margins (names of dimensions and sub-dimensions) in generalized arrays, operators and utility functions provided in this package automatically match the margins, doing map-reduce style parallel computation along margins. Generalized arrays are also cooperative to R's native functions that work on simple arrays.
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2024-01-16 |
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r-gaparsimony
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Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.
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2024-01-16 |
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r-gamboostlss
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Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.
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2024-01-16 |
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r-gap.datasets
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Datasets associated with the 'gap' package. Currently, it includes an example data for regional association plot (CDKN), an example data for a genomewide association meta-analysis (OPG), data in studies of Parkinson's diease (PD), ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers and Schizophrenia (apoeapoc), cystic fibrosis (cf), a Olink/INF panel (inf1), Manhattan plots with (hr1420, mhtdata) and without (w4) gene annotations.
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2024-01-16 |
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r-ganpadata
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This is a dataset package for GANPA, which implements a network-based gene weighting approach to pathway analysis. This package includes data useful for GANPA, such as a functional association network, pathways, an expression dataset and multi-subunit proteins.
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2024-01-16 |
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r-gamrr
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To calculate the relative risk (RR) for the generalized additive model.
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2024-01-16 |
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r-gamm4
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Estimate generalized additive mixed models via a version of function gamm() from 'mgcv', using 'lme4' for estimation.
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2024-01-16 |
