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r-kere
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An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.
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2025-04-22 |
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r-kendall
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Computes the Kendall rank correlation and Mann-Kendall trend test. See documentation for use of block bootstrap when there is autocorrelation.
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2025-04-22 |
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r-kaps
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This package provides some routines to conduct the K-adaptive parititioning (kaps) algorithm for survival data. A function kaps is an implementation version of our algorithm.
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2025-04-22 |
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r-kappalab
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S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known M�bius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
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2025-04-22 |
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r-kamila
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Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou (2018) <doi:10.18637/jss.v083.i13>.
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2025-04-22 |
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r-jwutil
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This is a set of simple utilities for various data manipulation and testing tasks. The goal is to use core R tools well, without bringing in many dependencies. Main areas of interest are semi-automated data frame manipulation, such as converting factors in multiple binary indicator columns. There are testing functions which provide 'testthat' expectations to permute arguments to function calls. There are functions and data to test extreme numbers, dates, and bad input of various kinds which should allow testing failure and corner cases, which can be used for fuzzing your functions. The test suite has many examples of usage.
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2025-04-22 |
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r-junctions
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Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, <doi:10.1101/058107>) and Janzen (2020, <doi:10.1101/2020.09.10.292441>).
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2025-04-22 |
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r-jumptest
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A fast simulation on stochastic volatility model, with jump tests, p-values pooling, and FDR adjustments.
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2025-04-22 |
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r-jtgwas
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The core of this 'Rcpp' based package is a function to compute standardized Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, e.g., genome-wide analysis. It implements 'OpenMP', allowing the option of computing on multiple threads. Supporting functions are also provided to calculate p-values and summarize results.
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2025-04-22 |
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r-juliacall
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Provides an R interface to 'Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see <https://julialang.org/> for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any 'Julia' function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like 'Julia'.
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2025-04-22 |
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r-jrf
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Simultaneous estimation of multiple related networks.
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2025-04-22 |
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r-jousboost
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Implements under/oversampling for probability estimation. To be used with machine learning methods such as AdaBoost, random forests, etc.
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2025-04-22 |
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r-jointdiag
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Different algorithms to perform approximate joint diagonalization of a finite set of square matrices. Depending on the algorithm, orthogonal or non-orthogonal diagonalizer is found. These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.
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2025-04-22 |
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r-jmotif
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Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification.
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2025-04-22 |
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r-jmi
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Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.
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2025-04-22 |
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r-jade
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Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
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2025-04-22 |
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r-jmdl
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Fit joint mean-correlation models for discrete longitudinal data (Tang CY,Zhang W, Leng C, 2017 <doi:10.5705/ss.202016.0435>).
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2025-04-22 |
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r-jaguar
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Implements a novel score test that measures 1) the overall shift in the gene expression due to genotype (additive genetic effect), and 2) group-specific changes in gene expression due to genotype (interaction effect) in a mixed-effects model framework.
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2025-04-22 |
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r-jaspar
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R modules for JASPAR data processing and visualization
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2025-04-22 |
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r-itree
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This package is based on the code of the rpart package. It extends rpart by adding additional splitting methods emphasizing interpretable/parsimonious trees. Unless indicated otherwise, it is safe to assume that all functions herein are extensions of or copied directly from similar or nearly identical rpart methods. As such, the authors of rpart are authors of this package as well. However, please direct any error reports or other questions about itree to the maintainer of this package; they are welcome and appreciated.
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2025-04-22 |
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r-jacobieigen
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Implements the classical Jacobi algorithm for the eigenvalues and eigenvectors of a real symmetric matrix, both in pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example for teaching purposes.
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2025-04-22 |
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r-isr3
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Performs multivariate normal imputation through iterative sequential regression. Conditional dependency structure between imputed variables can be specified a priori to accelerate imputation.
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2025-04-22 |
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r-itrlearn
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Maximin-projection learning (MPL, Shi, et al., 2018) is implemented for recommending a meaningful and reliable individualized treatment regime for future groups of patients based on the observed data from different populations with heterogeneity in individualized decision making. Q-learning and A-learning are implemented for estimating the groupwise contrast function that shares the same marginal treatment effects. The packages contains classical Q-learning and A-learning algorithms for a single stage study as a byproduct. More functions will be added at later versions.
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2025-04-22 |
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r-isoband
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A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.
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2025-04-22 |
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r-isospecr
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IsoSpec is a fine structure calculator used for obtaining the most probable masses of a chemical compound given the frequencies of the composing isotopes and their masses. It finds the smallest set of isotopologues with a given probability. The probability is assumed to be that of the product of multinomial distributions, each corresponding to one particular element and parametrized by the frequencies of finding these elements in nature. These numbers are supplied by IUPAC - the International Union of Pure and Applied Chemistry. See: Lacki, Valkenborg, Startek (2020) <DOI:10.1021/acs.analchem.0c00959> and Lacki, Startek, Valkenborg, Gambin (2017) <DOI:10.1021/acs.analchem.6b01459> for the description of the algorithms used.
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2025-04-22 |
