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r-horseshoe
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Contains functions for applying the horseshoe prior to high- dimensional linear regression, yielding the posterior mean and credible intervals, amongst other things. The key parameter tau can be equipped with a prior or estimated via maximum marginal likelihood estimation (MMLE). The main function, horseshoe, is for linear regression. In addition, there are functions specifically for the sparse normal means problem, allowing for faster computation of for example the posterior mean and posterior variance. Finally, there is a function available to perform variable selection, using either a form of thresholding, or credible intervals.
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2023-06-16 |
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r-highscreen
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Can be used to carry out extraction, normalization, quality control (QC), candidate hits identification and visualization for plate based assays, in drug discovery. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
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2023-06-16 |
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r-hiertest
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Implementation of the convex hierarchical testing (CHT) procedure introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.
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2023-06-16 |
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r-gwidgets2
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Re-implementation of the 'gWidgets' API. The API is defined in this package. A second, toolkit-specific package is required to use it. There are three in development: 'gWidgets2RGtk2', 'gWidgets2Qt', and 'gWidgets2tcltk'.
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2023-06-16 |
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r-gustave
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Provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.
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2023-06-16 |
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r-growthrate
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A nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.
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2023-06-16 |
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r-gridbezier
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Functions for rendering Bezier curves (Pomax, 2018) <https://pomax.github.io/bezierinfo/> in 'grid'. There is support for both quadratic and cubic Bezier curves. There are also functions for calculating points on curves, tangents to curves, and normals to curves.
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2023-06-16 |
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r-graticule
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Create graticule lines and labels for maps. Control the creation of lines by setting their placement (at particular meridians and parallels) and extent (along parallels and meridians). Labels are created independently of lines.
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2023-06-16 |
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r-gramevol
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A native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.
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2023-06-16 |
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r-glassdoor
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Interacts with the 'Glassdoor' API <https://www.glassdoor.com/developer/index.htm>. Allows the user to search job statistics, employer statistics, and job progression, where 'Glassdoor' provides a breakdown of other jobs a person did after their current one.
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2023-06-16 |
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r-gensemble
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Generalized ensemble methods allowing arbitrary underlying models to be used. Currently only bagging is supported.
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-gaussianhmm1d
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Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.
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2023-06-16 |
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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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2023-06-16 |
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r-lazydata
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Supplies a LazyData facility for packages which have data sets but do not provide LazyData: true. A single function is is included, requireData, which is a drop-in replacement for base::require, but carrying the additional functionality. By default, it suppresses package startup messages as well. See argument 'reallyQuitely'.
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2023-06-16 |
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r-lamme
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Log-analytic methods intended for testing multiplicative effects.
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2023-06-16 |
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r-kseaapp
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Infers relative kinase activity from phosphoproteomics data using the method described by Casado et al. (2013) <doi:10.1126/scisignal.2003573>.
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2023-06-16 |
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r-knitlatex
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Provides several helper functions for working with 'knitr' and 'LaTeX'. It includes 'xTab' for creating traditional 'LaTeX' tables, 'lTab' for generating 'longtable' environments, and 'sTab' for generating a 'supertabular' environment. Additionally, this package contains a knitr_setup() function which fixes a well-known bug in 'knitr', which distorts the 'results="asis"' command when used in conjunction with user-defined commands; and a com command (<<com=TRUE>>=) which renders the output from 'knitr' as a 'LaTeX' command.
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2023-06-16 |
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r-kimisc
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A collection of useful functions not found anywhere else, mainly for programming: Pretty intervals, generalized lagged differences, checking containment in an interval, and an alternative interface to assign().
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2023-06-16 |
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r-itertools
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Various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the 'snow' package.
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2023-06-16 |
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r-iterpc
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Iterator for generating permutations and combinations. They can be either drawn with or without replacement, or with distinct/ non-distinct items (multiset). The generated sequences are in lexicographical order (dictionary order). The algorithms to generate permutations and combinations are memory efficient. These iterative algorithms enable users to process all sequences without putting all results in the memory at the same time. The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer being maintained. Users are recommended to switch to 'arrangements'.
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2023-06-16 |
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r-interva5
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Provides an R version of the 'InterVA5' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
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2023-06-16 |
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r-inspectr
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Check one column or multiple columns of a dataframe using the preset basic checks or your own functions. Enables checks without knowledge of lapply() or sapply().
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-imis
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IMIS algorithm draws samples from the posterior distribution. The user has to define the following R functions in advance: prior(x) calculates prior density of x, likelihood(x) calculates the likelihood of x, and sample.prior(n) draws n samples from the prior distribution.
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2023-06-16 |
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r-imgw
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Download Polish meteorological and hydrological data from the Institute of Meteorology and Water Management - National Research Institute (<https://dane.imgw.pl/>). This package also allows for adding geographical coordinates for each observation.
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2023-06-16 |
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r-icesvocab
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R interface to access the RECO POX web services of the ICES (International Council for the Exploration of the Sea) Vocabularies database <https://vocab.ices.dk/services/POX.aspx>.
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2023-06-16 |
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r-icarus
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Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration.
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2023-06-16 |
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r-ibdlabels
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Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
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2023-06-16 |
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r-iatanalytics
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Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). A sample dataset (titled 'sampledata') is included in this package to practice with.
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2023-06-16 |
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r-iadapt
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Simulate and implement early phase two-stage adaptive dose-finding design developed by Chiuzan et al. (2018) <DOI:10.1080/19466315.2018.1462727>.
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2023-06-16 |
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r-hwriter
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Easy-to-use and versatile functions to output R objects in HTML format
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2023-06-16 |
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r-hsiccca
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Canonical correlation analysis that extracts nonlinear correlation through the use of Hilbert Schmidt Independence Criterion and Centered Kernel Target Alignment.
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2023-06-16 |
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r-homeric
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A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.
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2023-06-16 |
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r-hmdhfdplus
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Utilities for reading data from the Human Mortality Database (<https://www.mortality.org>), Human Fertility Database (<https://www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<http://www.fertilitydata.org/>), The Japanese Mortality Database (<http://www.ipss.go.jp/p-toukei/JMD>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
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2023-06-16 |
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r-highttest
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Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
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2023-06-16 |
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r-helsinki
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Tools for accessing various open data sources in the Helsinki region in Finland. Current data sources include the Real Estate Department (<http://ptp.hel.fi/avoindata/>), Service Map API (<http://api.hel.fi/servicemap/v1/>), Linked Events API (<http://api.hel.fi/linkedevents/v0.1/>), Helsinki Region Infoshare statistics API (<https://dev.hel.fi/stats/>).
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2023-06-16 |
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r-hellno
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Base R's default setting for 'stringsAsFactors' within 'data.frame()' and 'as.data.frame()' is supposedly the most often complained about piece of code in the R infrastructure. The 'hellno' package provides an explicit solution without changing R itself or having to mess around with options. It tries to solve this problem by providing alternative 'data.frame()' and 'as.data.frame()' functions that are in fact simple wrappers around base R's 'data.frame()' and 'as.data.frame()' with 'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE ) by default.
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2023-06-16 |
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r-hedgehog
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Hedgehog will eat all your bugs. 'Hedgehog' is a property-based testing package in the spirit of 'QuickCheck'. With 'Hedgehog', one can test properties of their programs against randomly generated input, providing far superior test coverage compared to unit testing. One of the key benefits of 'Hedgehog' is integrated shrinking of counterexamples, which allows one to quickly find the cause of bugs, given salient examples when incorrect behaviour occurs.
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2023-06-16 |
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r-hbim
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Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
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2023-06-16 |
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r-hazer
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Provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.
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2023-06-16 |
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r-handtill2001
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An S4 implementation of Eq. (3) and Eq. (7) by David J. Hand and Robert J. Till (2001) <DOI:10.1023/A:1010920819831>.
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2023-06-16 |
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r-gym
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OpenAI Gym is a open-source Python toolkit for developing and comparing reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API, and enables access to an ever-growing variety of environments. For more details on OpenAI Gym, please see here: <https://github.com/openai/gym>. For more details on the OpenAI Gym API specification, please see here: <https://github.com/openai/gym-http-api>.
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2023-06-16 |
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r-gvcm.cat
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Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.
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2023-06-16 |
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r-gvc
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Several tools for Global Value Chain ('GVC') analysis are implemented.
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2023-06-16 |
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r-gsmx
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Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.
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2023-06-16 |
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r-gsloid
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Contains published data sets for global benthic d18O data for 0-5.3 Myr <doi:10.1029/2004PA001071> and global sea levels based on marine sediment core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.
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2023-06-16 |
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r-gsalib
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This package contains utility functions used by the Genome Analysis Toolkit (GATK) to load tables and plot data. The GATK is a toolkit for variant discovery in high-throughput sequencing data.
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2023-06-16 |
