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r-htm2txt
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Convert a html document to simple plain texts by removing all html tags. This package utilizes regular expressions to strip off html tags. It also offers gettxt() and browse() function, which enables you to get or browse texts at a certain web page.
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2023-06-16 |
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r-hotspot
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Contains data for software hotspot analysis, along with a function performing the analysis itself.
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2023-06-16 |
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r-hmmpa
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Analysing time-series accelerometer data to quantify length and intensity of physical activity using hidden Markov models. It also contains the traditional cut-off point method. Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014)<doi:10.1371/journal.pone.0114089>.
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2023-06-16 |
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r-hmm
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Easy to use library to setup, apply and make inference with discrete time and discrete space Hidden Markov Models
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2023-06-16 |
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r-higrad
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Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm, a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD). In addition, this method attaches a confidence interval to assess the uncertainty of its predictions. See Su and Zhu (2018) <arXiv:1802.04876> for details.
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2023-06-16 |
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r-hcr
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This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. We implement an effective solution to recover the above hidden compact representation under the likelihood framework. Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.
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2023-06-16 |
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r-harmonicregression
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Fits the first harmonics in a Fourier expansion to one or more time series. Trend elimination can be performed. Computed values include estimates of amplitudes and phases, as well as confidence intervals and p-values for the null hypothesis of Gaussian noise.
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2023-06-16 |
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r-gsaq
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Computation of Quantitative Trait Loci hits in the selected gene set. Performing gene set validation with Quantitative Trait Loci information. Performing gene set enrichment analysis with available Quantitative Trait Loci data and computation of statistical significance value from gene set analysis. Obtaining the list of Quantitative Trait Loci hit genes along with their overlapped Quantitative Trait Loci names.
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2023-06-16 |
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r-groupica
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Contains an implementation of an independent component analysis (ICA) for grouped data. The main function groupICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding for user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/groupICA/>.
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2023-06-16 |
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r-gmdhreg
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Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko. Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons. An introduction of GMDH algorithms: Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292> Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.
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2023-06-16 |
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r-glmtlp
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It provides an extremely efficient procedure for fitting the entire truncated lasso regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. The algorithm uses the difference of convex technique. The detail of the algorithm is described in Shen, Pan and Zhu (2012) <doi:10.1080/01621459.2011.645783>. The package is inherited from a popular R package 'glmnet' and many functions in 'glmnet' can be directly used in 'glmtlp'. You can learn more details by the online manual (<http://wuchong.org/glmtlp.html>).
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2023-06-16 |
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r-gesca
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Fit a variety of component-based structural equation models.
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-lbreg
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Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.
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2023-06-16 |
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r-languager
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Data sets exemplifying statistical methods, and some facilitatory utility functions used in ``Analyzing Linguistic Data: A practical introduction to statistics using R'', Cambridge University Press, 2008.
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2023-06-16 |
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r-languagelayer
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Improve your text analysis with languagelayer <https://languagelayer.com>, a powerful language detection API.
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2023-06-16 |
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r-laercio
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The package contains functions to compare and group means.
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2023-06-16 |
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r-kofdata
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Read Swiss time series data from the 'KOF Datenservice' API, <https://datenservice.kof.ethz.ch>. The API provides macroeconomic survey data, business cycle and further macro economic time series about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.
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2023-06-16 |
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r-knnindep
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This package provides the implementation of an exact formula of the ith nearest neighbour distance distribution and implementations of tests of independence based on that formula. Furthermore the package provides a general framework to benchmark tests of independence.
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2023-06-16 |
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r-keyringr
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Decrypts passwords stored in the Gnome Keyring, macOS Keychain and strings encrypted with the Windows Data Protection API.
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2023-06-16 |
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r-jsonarr
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This package enables users to access MongoDB by running queries and returning their results in R data frames. Usually, data in MongoDB is only available in the form of a JSON document. jSonarR uses data processing and conversion capabilities in the jSonar Analytics Platform and the JSON Studio Gateway (http://www.jsonstudio.com), to convert it to a tabular format which is easy to use with existing R packages.
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2023-06-16 |
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r-josaplay
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Josa in Korean is often determined by judging the previous word. When writing reports using Rmd, a function that prints the appropriate investigation for each case is helpful. The 'josaplay' package then evaluates the previous word to determine which josa is appropriate.
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2023-06-16 |
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r-isotonic.pen
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Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
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2023-06-16 |
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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 Rstudio-sponsored CRAN mirror and/or the USGS GRAN Repository. Lastly, the chosen package(s) is/are loaded. The function `load_package` simply loads the provided packages.
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2023-06-16 |
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r-infdim
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This package contains functions to perform calculations of the infine-dimensional model (IDM) and to produce 95% confidence intervals around the model elements through bootstrapping.
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2023-06-16 |
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r-iemisctext
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The eclectic collection includes the following written pieces: "The War Prayer" by Mark Twain, "War Is A Racket" by Major General Smedley Butler, "The Mask of Anarchy: Written on the Occasion of the Massacre at Manchester" by Percy Bysshe Shelley, "Connect the D.O.T.S." by Obiora Embry, "Untitled: Climate Strange" by Irucka Ajani Embry, and "Untitled: Us versus Them or People Screwing over Other People (as we all live on one Earth and there is no "us versus them" in the actual Ultimate Reality}" by Irucka Ajani Embry.
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2023-06-16 |
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r-icesdatras
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R interface to access the web services of the ICES (International Council for the Exploration of the Sea) DATRAS trawl survey database <https://datras.ices.dk/WebServices/Webservices.aspx>.
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2023-06-16 |
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r-icc.sample.size
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Provides functions to calculate the requisite sample size for studies where ICC is the primary outcome. Can also be used for calculation of power. In both cases it allows the user to test the impact of changing input variables by calculating the outcome for several different values of input variables. Based off the work of Zou. Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Statistics in medicine, 31(29), 3972-3981.
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2023-06-16 |
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r-hypercube
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Provides methods for organizing data in a hypercube (i.e. a multi-dimensional cube). Cubes are generated from molten data frames. Each cube can be manipulated with five operations: rotation (changeDimensionOrder()), dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()), and rolling up (remove.aggregation()).
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2023-06-16 |
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r-hurdlr
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When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.
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2023-06-16 |
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r-httptest
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Testing and documenting code that communicates with remote servers can be painful. Dealing with authentication, server state, and other complications can make testing seem too costly to bother with. But it doesn't need to be that hard. This package enables one to test all of the logic on the R sides of the API in your package without requiring access to the remote service. Importantly, it provides three contexts that mock the network connection in different ways, as well as testing functions to assert that HTTP requests were---or were not---made. It also allows one to safely record real API responses to use as test fixtures. The ability to save responses and load them offline also enables one to write vignettes and other dynamic documents that can be distributed without access to a live server.
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2023-06-16 |
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r-hpbayes
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This package provides all the functions necessary to estimate the 8 parameters of the Heligman Pollard mortality model using a Bayesian Melding procedure with IMIS as well as to convert those parameters into age-specifc probabilities of death and a corresponding life table
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2023-06-16 |
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r-hierdiversity
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Hierarchical group-wise partitioning of phenotypic diversity into within-group (alpha), among-group (beta), and pooled-total (gamma) components using Hill numbers. Turnover and overlap are also calculated as standardized alternatives to beta diversity. Hierarchical bootstrapping is used to approximate uncertainty around each diversity component.
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2023-06-16 |
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r-hdmd
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High Dimensional Molecular Data (HDMD) typically have many more variables or dimensions than observations or replicates (D>>N). This can cause many statistical procedures to fail, become intractable, or produce misleading results. This package provides several tools to reduce dimensionality and analyze biological data for meaningful interpretation of results. Factor Analysis (FA), Principal Components Analysis (PCA) and Discriminant Analysis (DA) are frequently used multivariate techniques. However, PCA methods prcomp and princomp do not reflect the proportion of total variation of each principal component. Loadings.variation displays the relative and cumulative contribution of variation for each component by accounting for all variability in data. When D>>N, the maximum likelihood method cannot be applied in FA and the the principal axes method must be used instead, as in factor.pa of the psych package. The factor.pa.ginv function in this package further allows for a singular covariance matrix by applying a general inverse method to estimate factor scores. Moreover, factor.pa.ginv removes and warns of any variables that are constant, which would otherwise create an invalid covariance matrix. Promax.only further allows users to define rotation parameters during factor estimation. Similar to the Euclidean distance, the Mahalanobis distance estimates the relationship among groups. pairwise.mahalanobis computes all such pairwise Mahalanobis distances among groups and is useful for quantifying the separation of groups in DA. Genetic sequences are composed of discrete alphabetic characters, which makes estimates of variability difficult. MolecularEntropy and MolecularMI calculate the entropy and mutual information to estimate variability and covariability, respectively, of DNA or Amino Acid sequences. Functional grouping of amino acids (Atchley et al 1999) is also available for entropy and mutual information estimation. Mutual information values can be normalized by NMI to account for the background distribution arising from the stochastic pairing of independent, random sites. Alternatively, discrete alphabetic sequences can be transformed into biologically informative metrics to be used in various multivariate procedures. FactorTransform converts amino acid sequences using the amino acid indices determined by Atchley et al 2005.
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2023-06-16 |
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r-hbsae
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Functions to compute small area estimates based on a basic area or unit-level model. The model is fit using restricted maximum likelihood, or in a hierarchical Bayesian way. In the latter case numerical integration is used to average over the posterior density for the between-area variance. The output includes the model fit, small area estimates and corresponding MSEs, as well as some model selection measures. Additional functions provide means to compute aggregate estimates and MSEs, to minimally adjust the small area estimates to benchmarks at a higher aggregation level, and to graphically compare different sets of small area estimates.
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2023-06-16 |
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r-hamlet
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Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.
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2023-06-16 |
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r-gxm
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Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
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2023-06-16 |
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r-grcdata
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We implement two main functions. The first function uses a given grouped and/or right-censored grouping scheme and empirical data to infer parameters, and implements chi-square goodness-of-fit tests. The second function searches for the global optimal grouping scheme of grouped and/or right-censored count responses in surveys.
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2023-06-16 |
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r-gpdtest
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This package computes the bootstrap goodness-of-fit test for the generalized Pareto distribution by Villasenor-Alva and Gonzalez-Estrada (2009). The null hypothesis includes heavy and non-heavy tailed gPd's. A function for fitting the gPd to data using the parameter estimation methods proposed in the same article is also provided.
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2023-06-16 |
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r-googleformr
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GET and POST data to Google Forms; an API to Google Forms, allowing users to POST data securely to Google Forms without needing authentication or permissioning.
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2023-06-16 |
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r-goodmankruskal
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Association analysis between categorical variables using the Goodman and Kruskal tau measure. This asymmetric association measure allows the detection of asymmetric relations between categorical variables (e.g., one variable obtained by re-grouping another).
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2023-06-16 |
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r-glmmrr
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Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular.
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2023-06-16 |
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r-ggmridge
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Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).
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2023-06-16 |
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r-gevcdn
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Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.
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2023-06-16 |
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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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2023-06-16 |
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r-labstatr
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Insieme di funzioni di supporto al volume "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. This package contains sets of functions defined in "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. Function names and docs are in italian as well.
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2023-06-16 |
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r-label.switching
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The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
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2023-06-16 |
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r-krls
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Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
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2023-06-16 |
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r-krige
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Estimates kriging models for geographical point-referenced data. Method is described in Monogan and Gill (2016) <doi:10.1017/psrm.2015.5>.
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2023-06-16 |
