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r-mmpf
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public |
Marginalizes prediction functions using Monte-Carlo integration and computes permutation importance.
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2025-04-22 |
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r-mmpa
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To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number of assays needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate different orders in which the sample would be collected to form pools. Using MC avoids the dependence of the estimated number of assays on any specific ordering of the samples to form pools.
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2025-04-22 |
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r-mmms
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The package implements a multi-marker molecular signature (MMMS) approach for treatment-specific subgroup identification.
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2025-04-22 |
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r-mmmgee
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Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
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2025-04-22 |
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r-mmeln
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Fit multivariate mixture of normal distribution using covariance structure.
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2025-04-22 |
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r-mme
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Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing independent time effect; Model 3, introducing correlated time effect. mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.
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2025-04-22 |
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r-mmds
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This library implements mixture model distance sampling methods. See Miller and Thomas (in prep.).
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2025-04-22 |
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r-mmc
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Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
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2025-04-22 |
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r-mmac
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Contains the data sets for the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The book will be published by Oxford University Press in 2018 with ISBN-13: 978-019882472.
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2025-04-22 |
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r-mltools
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A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
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2025-04-22 |
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r-mltest
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A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.
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2025-04-22 |
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r-mlsjunkgen
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Generate a stream of pseudo-random numbers generated using the MLS Junk Generator algorithm. Functions exist to generate single pseudo-random numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
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2025-04-22 |
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r-mlpugs
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An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications.
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2025-04-22 |
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r-mlphaser
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Phase haplotypes from genotypes based on a list of known haplotypes. Suited to highly diverse loci such as HLA.
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2025-04-22 |
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r-ml.msbd
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Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani and T. Stadler (2017) <doi:10.1101/215491>.
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2025-04-22 |
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r-mlml2r
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Maximum likelihood estimates (MLE) of the proportions of 5-mC and 5-hmC in the DNA using information from BS-conversion, TAB-conversion, and oxBS-conversion methods. One can use information from all three methods or any combination of two of them. Estimates are based on Binomial model by Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and Kiihl et al. (2019) <doi:10.1515/sagmb-2018-0031>.
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2025-04-22 |
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r-mlmetrics
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A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.
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2025-04-22 |
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r-mlid
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Tools and functions to fit a multilevel index of dissimilarity.
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2025-04-22 |
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r-mlica2
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An R code implementation of the maximum likelihood (fixed point) algorithm of Hyvaerinen, Karhuna, and Oja for independent component analysis.
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2025-04-22 |
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r-mlf
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Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.
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2025-04-22 |
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r-mleur
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Provides functions for unit root testing using MLE method
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2025-04-22 |
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r-mle.tools
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Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.
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2025-04-22 |
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r-mlearning
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This package provides a unified interface to various machine learning algorithms. Confusion matrices are provided too.
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2025-04-22 |
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r-mlds
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Difference scaling is a method for scaling perceived supra-threshold differences. The package contains functions that allow the user to design and run a difference scaling experiment, to fit the resulting data by maximum likelihood and test the internal validity of the estimated scale.
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2025-04-22 |
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r-mldr.datasets
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Large collection of multilabel datasets along with the functions needed to export them to several formats, to make partitions, and to obtain bibliographic information.
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2025-04-22 |
