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r-integratebs
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Integrated B-spline function.
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2024-01-16 |
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r-intccr
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Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi{10.1002/sim.7350} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
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2024-01-16 |
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r-intcal
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The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.
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2024-01-16 |
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r-insurancedata
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Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
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2024-01-16 |
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r-instar
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Provides an interface to the Instagram API <https://instagram.com/ developer/>, which allows R users to download public pictures filtered by hashtag, popularity, user or location, and to access public users' profile data.
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2024-01-16 |
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r-installr
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R is great for installing software. Through the 'installr' package you can automate the updating of R (on Windows, using updateR()) and install new software. Software installation is initiated through a GUI (just run installr()), or through functions such as: install.Rtools(), install.pandoc(), install.git(), and many more. The updateR() command performs the following: finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation.
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2024-01-16 |
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r-install.load
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The function 'install_load' checks the local R library(ies) to see if the required package(s) is/are installed or not. If the package(s) is/are not installed, then the package(s) will be installed along with the required dependency(ies). This function pulls source or binary packages from the Posit/RStudio-sponsored CRAN mirror. Lastly, the chosen package(s) is/are loaded. The function 'load_package' simply loads the provided package(s). If this package does not fit your needs, then you may want to consider these other R packages: 'needs', 'easypackages', 'pacman', 'pak', 'anyLib', and/or 'librarian'.
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2024-01-16 |
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r-insight
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A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.
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2024-01-16 |
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r-inspectchangepoint
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Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.
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2024-01-16 |
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r-infusion
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Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated. A first approach was described in Rousset et al. (2017 <doi:10.1111/1755-0998.12627>) but the package implements more advanced methods.
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2024-01-16 |
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r-inlabru
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Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.
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2024-01-16 |
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r-information
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Performs exploratory data analysis and variable screening for binary classification models using weight-of-evidence (WOE) and information value (IV). In order to make the package as efficient as possible, aggregations are done in data.table and creation of WOE vectors can be distributed across multiple cores. The package also supports exploration for uplift models (NWOE and NIV).
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2024-01-16 |
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r-inline
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Functionality to dynamically define R functions and S4 methods with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling conventions.
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2024-01-16 |
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r-ingredients
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Collection of tools for assessment of feature importance and feature effects. Key functions are: feature_importance() for assessment of global level feature importance, ceteris_paribus() for calculation of the what-if plots, partial_dependence() for partial dependence plots, conditional_dependence() for conditional dependence plots, accumulated_dependence() for accumulated local effects plots, aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles, generic print() and plot() for better usability of selected explainers, generic plotD3() for interactive, D3 based explanations, and generic describe() for explanations in natural language. The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
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2024-01-16 |
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r-injector
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R dependency injection framework. Dependency injection allows a program design to follow the dependency inversion principle. The user delegates to external code (the injector) the responsibility of providing its dependencies. This separates the responsibilities of use and construction.
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2024-01-16 |
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r-ini
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Parse simple '.ini' configuration files to an structured list. Users can manipulate this resulting list with lapply() functions. This same structured list can be used to write back to file after modifications.
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2024-01-16 |
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r-infotrad
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Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
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2024-01-16 |
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r-infer
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The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.
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2024-01-16 |
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r-infodecompute
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The main purpose of this package is to generate the structure of the analysis of variance (ANOVA) table of the two-phase experiments. The user only need to input the design and the relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, this package can then quickly generate the structure of the ANOVA table with the coefficients of the variance components for the expected mean squares. Thus, the balanced incomplete block design and provides the efficiency factors of the fixed effects can also be studied and compared much easily.
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2024-01-16 |
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r-influence.me
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Provides a collection of tools for detecting influential cases in generalized mixed effects models. It analyses models that were estimated using 'lme4'. The basic rationale behind identifying influential data is that when single units are omitted from the data, models based on these data should not produce substantially different estimates. To standardize the assessment of how influential a (single group of) observation(s) is, several measures of influence are common practice, such as Cook's Distance. In addition, we provide a measure of percentage change of the fixed point estimates and a simple procedure to detect changing levels of significance.
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2024-01-16 |
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r-influence.sem
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A set of tools for evaluating several measures of case influence for structural equation models.
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2024-01-16 |
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r-indicspecies
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Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].
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2024-01-16 |
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r-inflection
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Implementation of methods Extremum Surface Estimator (ESE) and Extremum Distance Estimator (EDE) to identify the inflection point of a curve . Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> . Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> . Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
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2024-01-16 |
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r-infix
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Contains a number of infix binary operators that may be useful in day to day practices.
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2024-01-16 |
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r-infiniumpurify
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The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
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2024-01-16 |
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r-inferference
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Provides methods for estimating causal effects in the presence of interference described in B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
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2024-01-16 |
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r-imtest
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Implementation of the information matrix test for generalized partial credit models.
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2024-01-16 |
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r-inferencesmr
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Functions to make inference about the standardized mortality ratio (SMR) when evaluating the effect of a screening program. The package is based on methods described in Sasieni (2003) <doi: 10.1097/00001648-200301000-00026> and Talbot et al. (2011) <doi: 10.1002/sim.4334>.
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2024-01-16 |
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r-indtestpp
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It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence, and functions for generating and analysing different types of dependence.
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2024-01-16 |
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r-ineq
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Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
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2024-01-16 |
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r-indirect
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Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
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2024-01-16 |
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r-incidence
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Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
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2024-01-16 |
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r-incidence2
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Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence2' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis (<https://www.reconverse.org>).
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2024-01-16 |
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r-indexnumr
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Computes bilateral and multilateral index numbers. It has support for many standard bilateral indexes as well as multilateral index number methods such as GEKS, GEKS-Tornqvist (or CCDI), Geary-Khamis and the weighted time product dummy (for details on these methods see Diewert and Fox (2020) <doi:10.1080/07350015.2020.1816176>). It also supports updating of multilateral indexes using several splicing methods.
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2024-01-16 |
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r-iml
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Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>, accumulated local effects plots described by Apley (2018) <arXiv:1612.08468>, partial dependence plots described by Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
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2024-01-16 |
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r-inbreedr
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A framework for analysing inbreeding and heterozygosity-fitness correlations (HFCs) based on microsatellite and SNP markers.
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2024-01-16 |
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r-imputetestbench
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Provides a test bench for the comparison of missing data imputation methods in uni-variate time series. Imputation methods are compared using different error metrics. Proposed imputation methods and alternative error metrics can be used.
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2024-01-16 |
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r-implied
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Convert between bookmaker odds and probabilities. Eight different algorithms are available, including basic normalization, Shin's method (Hyun Song Shin, (1992) <doi:10.2307/2234526>), and others.
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2024-01-16 |
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r-imrmc
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Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. The initial package analyzes the reader-average area under the receiver operating characteristic (ROC) curve with U-statistics according to Gallas, Bandos, Samuelson, and Wagner 2009 <doi:10.1080/03610920802610084>. Additional functions analyze other endpoints with U-statistics (binary performance and score differences) following the work by Gallas, Pennello, and Myers 2007 <doi:10.1364/JOSAA.24.000B70>. Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.
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2024-01-16 |
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r-imputeyn
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Method brings less bias and more efficient estimates for AFT models.
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2024-01-16 |
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r-imputer
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Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
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2024-01-16 |
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r-importar
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Enables 'Python'-like importing/loading of packages or functions with aliasing to prevent namespace conflicts.
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2024-01-16 |
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r-ie2misc
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A collection of Irucka Embry's miscellaneous USGS functions (processing .exp and .psf files, statistical error functions, "+" dyadic operator for use with NA, creating ADAPS and QW spreadsheet files, calculating saturated enthalpy). Irucka created these functions while a Cherokee Nation Technology Solutions (CNTS) United States Geological Survey (USGS) Contractor and/or USGS employee.
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2024-01-16 |
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r-import
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Alternative mechanism for importing objects from packages and R modules. The syntax allows for importing multiple objects with a single command in an expressive way. The import package bridges some of the gap between using library (or require) and direct (single-object) imports. Furthermore the imported objects are not placed in the current environment.
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2024-01-16 |
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r-implyr
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'SQL' back-end to 'dplyr' for Apache Impala, the massively parallel processing query engine for Apache 'Hadoop'. Impala enables low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', and Dell 'EMC' 'Isilon'. See <https://impala.apache.org> for more information about Impala.
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2024-01-16 |
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r-impimp
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Imputing blockwise missing data by imprecise imputation, featuring a domain-based, variable-wise, and case-wise strategy. Furthermore, the estimation of lower and upper bounds for unconditional and conditional probabilities based on the obtained imprecise data is implemented. Additionally, two utility functions are supplied: one to check whether variables in a data set contain set-valued observations; and another to merge two already imprecisely imputed data. The method is described in a technical report by Endres, Fink and Augustin (2018, <doi:10.5282/ubm/epub.42423>).
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2024-01-16 |
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r-impact
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Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.
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2024-01-16 |
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r-immailgun
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Send emails using the 'mailgun' api. To use this package you will need an account from <https://www.mailgun.com> .
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2024-01-16 |
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r-imgur
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A complete API client for the image hosting service Imgur.com, including the an imgur graphics device, enabling the easy upload and sharing of plots.
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2024-01-16 |
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r-ifatools
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Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'. This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
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2024-01-16 |
