|
r-namespace
|
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)
|
2025-03-25 |
|
r-nam
|
public |
Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
|
2025-03-25 |
|
r-nadiv
|
public |
Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<http://www.vsni.co.uk/software/asreml>).
|
2025-03-25 |
|
r-nabor
|
public |
An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 'libnabo' has speed and space advantages over the 'ANN' library wrapped by package 'RANN'. 'nabor' includes a knn function that is designed as a drop-in replacement for 'RANN' function nn2. In addition, objects which include the k-d tree search structure can be returned to speed up repeated queries of the same set of target points.
|
2025-03-25 |
|
r-n1qn1
|
public |
Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS "qn" without constraints and 'qnbd' or Quasi-Newton BFGS with constraints. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. Both algorithms are described in the 'Scilab' optimization documentation located at <http://www.scilab.org/content/download/250/1714/file/optimization_in_scilab.pdf>.
|
2025-03-25 |
|
r-mwaved
|
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>.
|
2025-03-25 |
|
r-mvt
|
public |
Routines to perform estimation and inference under the multivariate t-distribution. Currently, the following methodologies are implemented: multivariate mean and covariance estimation, hypothesis testing about the mean, equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate generation. Some auxiliary functions are also provided.
|
2025-03-25 |
|
r-mvrtn
|
public |
Mean, variance, and random variates for left/right truncated normal distributions.
|
2025-03-25 |
|
r-mvnmle
|
public |
Finds the maximum likelihood estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values.
|
2025-03-25 |
|
r-mvnfast
|
public |
Provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API.
|
2025-03-25 |
|
r-mvna
|
public |
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.
|
2025-03-25 |
|
r-mvcluster
|
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.
|
2025-03-25 |
|
r-mvb
|
public |
Fit log-linear model for multivariate Bernoulli distribution with mixed effect models and LASSO
|
2025-03-25 |
|
r-mvar.pt
|
public |
Pacote para analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.
|
2025-03-25 |
|
r-mvar
|
public |
Package for multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.
|
2025-03-25 |
|
r-multnonparam
|
public |
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics.
|
2025-03-25 |
|
r-multivariaterandomforest
|
public |
Models and predicts multiple output features in single random forest considering the linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.
|
2025-03-25 |
|
r-multivariance
|
public |
Distance multivariance is a measure of dependence which can be used to detect and quantify dependence. The necessary functions are implemented in this packages, and examples are given. For the theoretic background we refer to the papers: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors: generalized distance covariance and Gaussian covariance. VMSTA, 2018, Vol. 5, No. 3, 353-383. <arXiv:1711.07778>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Distance multivariance: New dependence measures for random vectors. <arXiv:1711.07775>. G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. <arXiv:1808.07280>.
|
2025-03-25 |
|
r-multitaper
|
public |
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.
|
2025-03-25 |
|
r-multispatialccm
|
public |
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
|
2025-03-25 |
|
r-multinet
|
public |
Functions for the creation/generation and analysis of multilayer social networks.
|
2025-03-25 |
|
r-multifit
|
public |
Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky and Ma (2018) <arXiv:1806.06777>.
|
2025-03-25 |
|
r-multicool
|
public |
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. (2009) <DOI:10.1145/1496770.1496877> Loopless Generation of Multiset Permutations by Prefix Shifts. Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from <http://tamivox.org/dave/multinomial/code.html> and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here <http://jeromekelleher.net/tag/integer-partitions.html>. The C++ code and Python code are distributed without conditions.
|
2025-03-25 |
|
r-multicnvdetect
|
public |
This package provides a tool for analysis of multiple CNV.
|
2025-03-25 |
|
r-muhaz
|
public |
Produces a smooth estimate of the hazard function for censored data.
|
2025-03-25 |
|
r-mudens
|
public |
Compute a density estimate from a vector of right-censored survival time using kernel functions.
|
2025-03-25 |
|
r-muchpoint
|
public |
Nonparametric approach to estimate the location of block boundaries (change-points) of non-overlapping blocks in a random symmetric matrix which consists of random variables whose distribution changes from block to block. BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) <doi:10.1016/j.jmva.2017.12.005>.
|
2025-03-25 |
|
r-mts
|
public |
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index, transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
|
2025-03-25 |
|
r-mtlr
|
public |
An implementation of Multi-Task Logistic Regression (MTLR) for R. This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.
|
2025-03-25 |
|
r-mstate
|
public |
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.
|
2025-03-25 |
|
r-mssm
|
public |
Provides methods to perform parameter estimation and make analysis of multivariate observed outcomes through time which depends on a latent state variable. All methods scale well in the dimension of the observed outcomes at each time point. The package contains an implementation of a Laplace approximation, particle filters like suggested by Lin, Zhang, Cheng, & Chen (2005) <doi:10.1198/016214505000000349>, and the gradient and observed information matrix approximation suggested by Poyiadjis, Doucet, & Singh (2011) <doi:10.1093/biomet/asq062>.
|
2025-03-25 |
|
r-msimcc
|
public |
Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.
|
2025-03-25 |
|
r-msgps
|
public |
Computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. The optimal model can be selected by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC.
|
2025-03-25 |
|
r-msglasso
|
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.
|
2025-03-25 |
|
r-msde
|
public |
Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components. The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
|
2025-03-25 |
|
r-msda
|
public |
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.
|
2025-03-25 |
|
r-msbp
|
public |
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).
|
2025-03-25 |
|
r-mrs
|
public |
An implementation of the MRS algorithm for comparison across distributions, as described in Jacopo Soriano, Li Ma (2016) <doi:10.1111/rssb.12180>. The model is based on a nonparametric process taking the form of a Markov model that transitions between a "null" and an "alternative" state on a multi-resolution partition tree of the sample space. MRS effectively detects and characterizes a variety of underlying differences. These differences can be visualized using several plotting functions.
|
2025-03-25 |
|
r-mrmre
|
public |
"Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique with DOI: N De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>."
|
2025-03-25 |
|
r-mrm
|
public |
Conditional maximum likelihood estimation via the EM algorithm and information-criterion-based model selection in binary mixed Rasch models.
|
2025-03-25 |
|
r-mrfse
|
public |
A Markov random field structure estimator that uses a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion (Frondana, 2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.
|
2025-03-25 |
|
r-mra
|
public |
Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) <doi:10.2307/2556151>; Jolly (1965) <doi:10.2307/2333826>; Seber (1965) <doi:10.2307/2333827>) and Huggin's (1989) <doi:10.2307/2336377> closed population. Link functions include logit, sine, and hazard. Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) <doi:10.2307/2280784> estimator.
|
2025-03-25 |
|
r-mptinr
|
public |
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
|
2025-03-25 |
|
r-mpsem
|
public |
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
|
2025-03-25 |
|
r-mpmi
|
public |
Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets.
|
2025-03-25 |
|
r-mp
|
public |
Multidimensional projection techniques are used to create two dimensional representations of multidimensional data sets.
|
2025-03-25 |
|
r-monreg
|
public |
Estimates monotone regression and variance functions in a nonparametric model.
|
2025-03-25 |
|
r-monopoly
|
public |
Functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and Turlach (2016) <doi:10.1080/00949655.2016.1139582>.
|
2025-03-25 |
|
r-monomvn
|
public |
Estimation of multivariate normal and student-t data of arbitrary dimension where the pattern of missing data is monotone. Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided.
|
2025-03-25 |
|
r-monetdb.r
|
public |
Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.
|
2025-03-25 |
