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r-iilasso
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public |
Efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso. Takada, M., Suzuki, T., & Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. AISTATS. <http://proceedings.mlr.press/v84/takada18a/takada18a.pdf>.
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2023-06-16 |
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r-ifa
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public |
The package performes Independent Factor Analysis
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2023-06-16 |
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r-idmtpreg
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public |
Modeling of regression effects for transition probabilities in a progressive illness-death model. Azarang, Scheike, and de Una-Alvarez (2017) <doi:10.1002/sim.7245>.
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2023-06-16 |
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r-identity
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public |
Calculate identity coefficients, based on Mark Abney's C code.
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2023-06-16 |
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r-icemelt
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public |
Estimates the parameters of the semiparametric linear transformation model using imputation method, naive method and regression calibration method when time-to-event is interval-censored and a covariate is measured with error. A right censoring indicator must be available. The methods implemented in this package can be found in Mandal, S., Wang, S. and Sinha, S. (2019+). Analysis of Linear Transformation Models with Covariate Measurement Error and Interval Censoring. (accepted, Statistics In Medicine).
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2023-06-16 |
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r-excel.link
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public |
Allows access to data in running instance of Microsoft Excel (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with 'xl[a1] = current.graphics()'. Additionally there are function for reading/writing 'Excel' files - 'xl.read.file'/'xl.save.file'. They are not fast but able to read/write '*.xlsb'-files and password-protected files. There is an Excel workbook with examples of calling R from Excel in the 'doc' folder. It tries to keep things as simple as possible - there are no needs in any additional installations besides R, only 'VBA' code in the Excel workbook. Microsoft Excel is required for this package.
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2023-06-16 |
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openssl
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public |
OpenSSL is an open-source implementation of the SSL and TLS protocols
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2023-06-16 |
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r-rdsm
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public |
Provides a threads-type programming environment for R. The package gives the R programmer the clearer, more concise shared memory world view, and in some cases gives superior performance as well. In addition, it enables parallel processing on very large, out-of-core matrices.
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2023-06-16 |
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r-exporkit
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public |
An R-interface to the Fortran package Expokit.
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2023-06-16 |
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r-exactloglintest
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public |
Monte Carlo and MCMC goodness of fit tests for log-linear models
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2023-06-16 |
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r-estudy2
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public |
An implementation of a most commonly used event study methodology, including both parametric and nonparametric tests. It contains variety aspects of the rate of return estimation (the core calculation is done in C++), as well as three classical for event study market models: mean adjusted returns, market adjusted returns and single-index market models. There are 6 parametric and 6 nonparametric tests provided, which examine cross-sectional daily abnormal return (see the documentation of the functions for more information). Parametric tests include tests proposed by Brown and Warner (1980) <DOI:10.1016/0304-405X(80)90002-1>, Brown and Warner (1985) <DOI:10.1016/0304-405X(85)90042-X>, Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Patell (1976) <DOI:10.2307/2490543>, and Lamb (1995) <DOI:10.2307/253695>. Nonparametric tests covered in estudy2 are tests described in Corrado and Zivney (1992) <DOI:10.2307/2331331>, McConnell and Muscarella (1985) <DOI:10.1016/0304-405X(85)90006-6>, Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Cowan (1992) <DOI:10.1007/BF00939016>, Corrado (1989) <DOI:10.1016/0304-405X(89)90064-0>, Campbell and Wasley (1993) <DOI:10.1016/0304-405X(93)90025-7>, Savickas (2003) <DOI:10.1111/1475-6803.00052>, Kolari and Pynnonen (2010) <DOI:10.1093/rfs/hhq072>. Furthermore, tests for the cumulative abnormal returns proposed by Brown and Warner (1985) <DOI:10.1016/0304-405X(85)90042-X> and Lamb (1995) <DOI:10.2307/253695> are included.
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2023-06-16 |
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r-esther
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public |
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
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2023-06-16 |
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r-erboost
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public |
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
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2023-06-16 |
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r-elec.strat
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public |
An extension of the elec package intended for use on election audits using stratified random samples. Includes functions to obtain conservative and exact p-values, and functions that give sample sizes that may make election audits more efficient.
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2023-06-16 |
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r-arrow
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public |
'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the 'Arrow C++' library.
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2023-06-16 |
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numpy-devel
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public |
No Summary
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2023-06-16 |
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numpy-base
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public |
No Summary
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2023-06-16 |
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numpy
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public |
Array processing for numbers, strings, records, and objects.
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2023-06-16 |
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mkl_fft
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public |
NumPy-based implementation of Fast Fourier Transform using Intel (R) Math Kernel Library.
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2023-06-16 |
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mkl_random
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public |
Intel (R) MKL-powered package for sampling from common probability distributions into NumPy arrays.
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2023-06-16 |
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r-msglasso
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public |
For fitting multivariate response and multiple predictor linear regressions with an arbitrary group structure assigned on the regression coefficient matrix, using the multivariate sparse group lasso and the mixed coordinate descent algorithm.
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2023-06-16 |
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r-mwaved
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public |
Computes the Wavelet deconvolution estimate of a common signal present in multiple channels that have possible different levels of blur and long memory additive error, see Kulik, Sapatinas and Wishart (2015), <doi:10.1016/j.acha.2014.04.004>.
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2023-06-16 |
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r-mixeddataimpute
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public |
Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local dependence described in Murray and Reiter (2015); see 'citation("MixedDataImpute")' or http://arxiv.org/abs/1410.0438). See '?hcmm_impute' for example usage.
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2023-06-16 |
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r-mudens
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public |
Compute a density estimate from a vector of right-censored survival time using kernel functions.
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2023-06-16 |
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r-mixtureregltic
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public |
Fit mixture regression models with nonsusceptibility/cure for left-truncated and interval-censored (LTIC) data (see Chen et al. (2013) <doi:10.1002/sim.5845>). This package also provides the nonparametric maximum likelihood estimator (NPMLE) for the survival/event curves with LTIC data.
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2023-06-16 |
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r-mvrtn
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public |
Mean, variance, and random variates for left/right truncated normal distributions.
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2023-06-16 |
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r-mnormpow
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public |
Computes integral of f(x)*x_i^k on a product of intervals, where f is the density of a gaussian law. This a is small alteration of the mnormt code from A. Genz and A. Azzalini.
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2023-06-16 |
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r-mlmmm
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public |
Computational strategies for multivariate linear mixed-effects models with missing values, Schafer and Yucel (2002), Journal of Computational and Graphical Statistics, 11, 421-442.
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2023-06-16 |
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r-mixor
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public |
Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
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2023-06-16 |
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r-mht
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public |
Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
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2023-06-16 |
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r-mvb
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public |
Fit log-linear model for multivariate Bernoulli distribution with mixed effect models and LASSO
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2023-06-16 |
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r-moc
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public |
Fits and vizualize user defined finite mixture models for multivariate observations using maximum likelihood. (McLachlan, G., Peel, D. (2000) Finite Mixture Models. Wiley-Interscience.)
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2023-06-16 |
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r-mvcluster
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public |
Implementation of multi-view bi-clustering algorithms. When a sample is characterized by two or more sets of input features, it creates multiple data matrices for the same set of examples, each corresponding to a view. For instance, individuals who are diagnosed with a disorder can be described by their clinical symptoms (one view) and their genomic markers (another view). Rows of a data matrix correspond to examples and columns correspond to features. A multi-view bi-clustering algorithm groups examples (rows) consistently across the views and simultaneously identifies the subset of features (columns) in each view that are associated with the row groups. This mvcluster package includes three such methods. (1) MVSVDL1: multi-view bi-clustering based on singular value decomposition where the left singular vectors are used to identify row clusters and the right singular vectors are used to identify features (columns) for each row cluster. Each singular vector is regularized by the L1 vector norm. (2) MVLRRL0: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the so-called L0 vector norm (which is not really a vector norm). (3) MVLRRL1: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the L1 vector norm.
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2023-06-16 |
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r-namespace
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public |
This package provides user-level functions to manage namespaces not (yet) available in base R: 'registerNamespace', 'unregisterNamespace', 'makeNamespace', and 'getRegisteredNamespace' . ('makeNamespaces' is extracted from the R 'base' package source code: src/library/base/R/namespace.R)
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2023-06-16 |
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r-modelgood
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public |
Bootstrap cross-validation for ROC, AUC and Brier score to assess and compare predictions of binary status responses.
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2023-06-16 |
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r-metaheuristicfpa
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public |
A nature-inspired metaheuristics algorithm based on the pollination process of flowers. This R package makes it easy to implement the standard flower pollination algorithm for every user. The algorithm was first developed by Xin-She Yang in 2012 (<DOI:10.1007/978-3-642-32894-7_27>).
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2023-06-16 |
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r-memnet
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public |
Efficient implementations of network science tools to facilitate research into human (semantic) memory. In its current version, the package contains several methods to infer networks from verbal fluency data, various network growth models, diverse (switcher-) random walk processes, and tools to analyze and visualize networks. To deliver maximum performance the majority of the code is written in C++. For an application see: Wulff, D. U., Hills, T., & Mata, R. (2018) <doi:10.31234/osf.io/s73dp>.
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2023-06-16 |
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r-mirada
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public |
This package collects algorithms/functions developed for microRNA profiling data analyses. Analytical platforms include traditional hybridization microarray, CGH, beads-based microarray, and qRT-PCR array.
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2023-06-16 |
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r-multicnvdetect
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public |
This package provides a tool for analysis of multiple CNV.
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2023-06-16 |
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r-mrm
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public |
Conditional maximum likelihood estimation via the EM algorithm and information-criterion-based model selection in binary mixed Rasch models.
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2023-06-16 |
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r-vicmodel
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public |
The Variable Infiltration Capacity (VIC) model is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington (UW). The version of VIC source code used is of 5.0.1 on <https://github.com/UW-Hydro/VIC/>, see Hamman et al. (2018). Development and maintenance of the current official version of the VIC model at present is led by the UW Hydro (Computational Hydrology group) in the Department of Civil and Environmental Engineering at UW. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally. If you make use of this model, please acknowledge the appropriate references listed in the help page of this package or on the references page <http://vic.readthedocs.io/en/master/Documentation/References/> of the VIC official documentation website. These should include Liang et al. (1994) plus any references relevant to the features you are using Reference: Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99(D7), 14415-14428, <doi:10.1029/94JD00483>. Hamman et al. (2018) about VIC 5.0.1 also can be considered: Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, <doi:10.5194/gmd-11-3481-2018>.
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2023-06-16 |
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r-weco
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public |
Western Electric Company Rules (WECO) have been widely used for Shewhart control charts in order to increase the sensitivity of detecting assignable causes of process change. This package implements eight commonly used WECO rules and allow to apply the combination of these individual rules for detecting the deviation from a stable process. The package also provides a web-based graphical user interface to help users conduct the analysis.
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2023-06-16 |
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r-vbdm
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public |
Efficient algorithm for solving discrete mixture regression model for rare variant association analysis. Uses variational Bayes algorithm to efficiently search over model space. Outputs an approximate likelihood ratio test as well as variant level posterior probabilities of association.
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2023-06-16 |
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r-truncgof
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Goodness-of-fit tests and some adjusted exploratory tools allowing for left truncated data
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2023-06-16 |
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r-vifcp
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Contains a function to support the following paper: Xiaoping Shi, Xiang-Sheng Wang, Dongwei Wei, Yuehua Wu (2016), <DOI:10.1007/s00180-015-0587-5>, A sequential multiple change-point detection procedure via VIF regression, Computational Statistics, 31(2): 671-691.
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2023-06-16 |
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r-twocop
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This package implements the nonparametric test of equality between two copulas proposed by Remillard and Scaillet in their 2009 JMVA paper.
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2023-06-16 |
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r-unidoe
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Efficient procedures for constructing uniform design of experiments under various space-filling criteria. It is based on a stochastic and adaptive threshold accepting algorithm with flexible initialization, adaptive threshold, and stochastic evolution. The package may also construct the augmented uniform designs in a sequential manner.
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2023-06-16 |
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r-unfoldr
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Stereological unfolding of the joint size-shape-orientation distribution of spheroidal shaped particles.
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2023-06-16 |
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r-tvd
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Total Variation Denoising is a regularized denoising method which effectively removes noise from piecewise constant signals whilst preserving edges. This package contains a C++ implementation of Condat's very fast 1D squared error loss TVD algorithm. Additional methods and loss functions may be added in future versions.
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2023-06-16 |
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r-tvsmiss
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Use a regularization likelihood method to achieve variable selection purpose. Likelihood can be worked with penalty lasso, smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). Tuning parameter selection techniques include cross validation (CV), Bayesian information criterion (BIC) (low and high), stability of variable selection (sVS), stability of BIC (sBIC), and stability of estimation (sEST). More details see Jiwei Zhao, Yang Yang, and Yang Ning (2018) <arXiv:1703.06379> "Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data." Statistica Sinica.
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2023-06-16 |
