|
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.
|
2023-06-16 |
|
r-mnp
|
public |
Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). ``A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation,'' Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. <DOI:10.1016/j.jeconom.2004.02.002> Detailed examples are given in Imai and van Dyk (2005). ``MNP: R Package for Fitting the Multinomial Probit Model.'' Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. <DOI:10.18637/jss.v014.i03>.
|
2023-06-16 |
|
r-mixtureregltic
|
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.
|
2023-06-16 |
|
r-mixture
|
public |
An implementation of all 14 Gaussian parsimonious clustering models (GPCMs) for model-based clustering and model-based classification.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
r-mvnmle
|
public |
Finds the maximum likelihood estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
r-mrm
|
public |
Conditional maximum likelihood estimation via the EM algorithm and information-criterion-based model selection in binary mixed Rasch models.
|
2023-06-16 |
|
r-modelltest
|
public |
An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.
|
2023-06-16 |
|
r-misssbm
|
public |
When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM' adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in Tabouy, Barbillon and Chiquet (2019) <doi:10.1080/01621459.2018.1562934>.
|
2023-06-16 |
|
r-microseq
|
public |
Basic functions for microbial sequence data analysis. The idea is to use the basic R data structures as much as possible, without building complex data types.
|
2023-06-16 |
|
r-mgl
|
public |
An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961.
|
2023-06-16 |
|
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)
|
2023-06-16 |
|
r-mvna
|
public |
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.
|
2023-06-16 |
|
r-msda
|
public |
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.
|
2023-06-16 |
|
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).
|
2023-06-16 |
|
r-mixedmem
|
public |
Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.
|
2023-06-16 |
|
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>.
|
2023-06-16 |
|
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>.
|
2023-06-16 |
|
r-monetdb.r
|
public |
Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.
|
2023-06-16 |
|
r-mmeta
|
public |
A novel multivariate meta-analysis.
|
2023-06-16 |
|
r-mixsqp
|
public |
Provides optimization algorithms based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithms are expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly in large data sets. The algorithms are described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2018) <arXiv:1806.01412>.
|
2023-06-16 |
|
r-meteor
|
public |
A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modelling, particularly crop and crop disease modeling.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
r-mvb
|
public |
Fit log-linear model for multivariate Bernoulli distribution with mixed effect models and LASSO
|
2023-06-16 |
|
r-mlmmm
|
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.
|
2023-06-16 |
|
r-micefast
|
public |
Fast imputations under the object-oriented programming paradigm. There was used quantitative models with a closed-form solution. Thus package is based on linear algebra operations. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
|
2023-06-16 |
|
r-metadynminer
|
public |
Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037> to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy surfaces, minima and transition paths can be plotted to produce publication quality images.
|
2023-06-16 |
|
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>).
|
2023-06-16 |
|
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>.
|
2023-06-16 |
|
r-mnormpow
|
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.
|
2023-06-16 |
|
r-mm4lmm
|
public |
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Hunter, D. R., & Lange, K. (2004) <doi:10.1198/0003130042836>).
|
2023-06-16 |
|
r-mixor
|
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.
|
2023-06-16 |
|
r-mgsda
|
public |
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2015), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA, to appear,[DOI:10.1080/01621459.2015.1034318].
|
2023-06-16 |
|
r-mergetrees
|
public |
Aggregates a set of trees with the same leaves to create a consensus tree. The trees are typically obtained via hierarchical clustering, hence the hclust format is used to encode both the aggregated trees and the final consensus tree. The method is exact and proven to be O(nqlog(n)), n being the individuals and q being the number of trees to aggregate.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
r-mlr3misc
|
public |
Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
|
2023-06-16 |
|
r-mlbench
|
public |
A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.
|
2023-06-16 |
|
r-mudens
|
public |
Compute a density estimate from a vector of right-censored survival time using kernel functions.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
r-mpsem
|
public |
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
|
2023-06-16 |
|
r-mp
|
public |
Multidimensional projection techniques are used to create two dimensional representations of multidimensional data sets.
|
2023-06-16 |
|
r-mined
|
public |
This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2018). The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate.
|
2023-06-16 |
|
r-mhsmm
|
public |
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences. Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
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.
|
2023-06-16 |
|
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.
|
2023-06-16 |
