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r-mscombine
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Find common entities detected in both positive and negative ionization mode, delete this entity in the less sensible mode and combine both matrices.
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2023-06-16 |
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r-mpm
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Exploratory graphical analysis of multivariate data, specifically gene expression data with different projection methods: principal component analysis, correspondence analysis, spectral map analysis.
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2023-06-16 |
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r-mpcmp
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A collection of functions for estimation, testing and diagnostic checking for the mean-parametrized Conway-Maxwell Poisson (COM-Poisson) regression model of Huang (2017) <doi:10.1177/1471082X17697749>.
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-mkle
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Package for fast computation of the maximum kernel likelihood estimator (mkle)
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2023-06-16 |
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r-mixdist
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Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.
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2023-06-16 |
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r-misc3d
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A collection of miscellaneous 3d plots, including isosurfaces.
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2023-06-16 |
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r-mhctools
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Ten tools for analysis of major histocompatibility complex (MHC) data in non- model species. The functions are tailored for amplicon data sets that have been filtered using the 'dada2' method (for more information visit <https://benjjneb.github.io/dada2>), but even other data sets can be analyzed, if the data tables are formatted according to the description in each function. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The HpltFind() function infers putative haplotypes from families in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of the haplotype inference. The PapaDiv() function compares parent pairs in the data set and calculate their joint MHC diversity, taking into account sequence variants that occur in both parents. The CalcPdist() function calculates the p-distances from pairwise comparisons of all sequences in a data set, and mean p-distances of all pairwise comparisons within each sample in a data set. The function includes the options to specify which codons to compare and to calculate amino acid p-distances. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates a fasta file for each sample in the data set.
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2023-06-16 |
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r-metrics
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An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.
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2023-06-16 |
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r-metaheuristicopt
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An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>), whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).
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2023-06-16 |
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r-mcsim
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Identifies the optimal number of clusters by calculating the similarity between two clustering methods at the same number of clusters using the corrected indices of Rand and Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at which the index attain its maximum more frequently is a candidate for being the optimal number of clusters.
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2023-06-16 |
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r-mchtest
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Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.
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2023-06-16 |
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r-mazeinda
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Methods for calculating and testing the significance of pairwise monotonic association from and based on the work of Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one or multiple sets can be performed in parallel thanks to the packages 'foreach' and 'doMC'.
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2023-06-16 |
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r-mavtgsa
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This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance). If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated. If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too. Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented. The program computes the p-values and FDR (false discovery rate) q-values for all gene sets. The p-values for individual genes in a significant gene set are also listed. MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set. A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.
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2023-06-16 |
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r-mata
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Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence intervals, which are constructed using single-model estimators and model weights. See Turek and Fletcher (2012) <doi:10.1016/j.csda.2012.03.002> for details.
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2023-06-16 |
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r-madr
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Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.
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2023-06-16 |
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r-lotkaslaw
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Running Lotka's Law following Pao (1985)(DOI: 10.1016/0306-4573(85)90055-X). The Law is based around the proof that the number of authors making n contributions is about 1/n^{a} of those making one contribution.
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2023-06-16 |
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r-longmemo
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Datasets and Functionality from 'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall. Estimation of Hurst (and more) parameters for fractional Gaussian noise, 'fARIMA' and 'FEXP' models.
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2023-06-16 |
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r-llogistic
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Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and phi. The parameter m is the median of the distribution.
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2023-06-16 |
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r-linkim
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A linkage information based method for imputing missing diploid genotypes
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2023-06-16 |
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r-lga
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Tools for linear grouping analysis. Three user-level functions: gap, rlga and lga.
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2023-06-16 |
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r-learnn
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Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
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2023-06-16 |
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r-nlroot
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This is a package which can help you search for the root of a equation.
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2023-06-16 |
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r-nlirms
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Design of non-life insurance rate-making system with a frequency and a severity component based on the a posteriori criteria. The rate-making system is a general form of bonus-malus system introduced by Lemaire (1995), <doi:10.1007/978-94-011-0631-3> and Frangos and Vrontos (2001), <doi:10.2143/AST.31.1.991>.
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2023-06-16 |
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r-nhldata
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Each dataset contains scores for every game during a specific season of the NHL.
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2023-06-16 |
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r-nfactors
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Indices, heuristics and strategies to help determine the number of factors/components to retain: 1. Acceleration factor (af with or without Parallel Analysis); 2. Optimal Coordinates (noc with or without Parallel Analysis); 3. Parallel analysis (components, factors and bootstrap); 4. lambda > mean(lambda) (Kaiser, CFA and related); 5. Cattell-Nelson-Gorsuch (CNG); 6. Zoski and Jurs multiple regression (b, t and p); 7. Zoski and Jurs standard error of the regression coeffcient (sescree); 8. Nelson R2; 9. Bartlett khi-2; 10. Anderson khi-2; 11. Lawley khi-2 and 12. Bentler-Yuan khi-2.
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2023-06-16 |
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r-negbinbetabinreg
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The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.
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2023-06-16 |
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r-ncbit
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making NCBI taxonomic data locally available and searchable as an R object
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2023-06-16 |
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r-naturesounds
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Collection of example animal sounds for bioacoustic analysis.
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2023-06-16 |
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r-naepprimer
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Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
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2023-06-16 |
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r-mycor
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Perform correlation and linear regression test among the numeric fields in a data.frame automatically and make plots using pairs or lattice::parallelplot.
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2023-06-16 |
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r-mvnggrad
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Package for moving grid adjustment in plant breeding field trials.
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2023-06-16 |
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r-mvlm
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Allows a user to conduct multivariate multiple regression using analytic p-values rather than classic approximate F-tests.
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2023-06-16 |
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r-mustat
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Performs Wilcox rank sum test, Kruskal rank sum test, Friedman rank sum test and McNemar test.
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2023-06-16 |
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r-multiway
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Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
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2023-06-16 |
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r-multirdpg
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Fits the Multiple Random Dot Product Graph Model and performs a test for whether two networks come from the same distribution. Both methods are proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational and Graphical Statistics).
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2023-06-16 |
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r-mtsys
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Mahalanobis-Taguchi (MT) system is a collection of multivariate analysis methods developed for the field of quality engineering. MT system consists of two families depending on their purpose. One is a family of Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y. (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods for the family of MT methods and one basic method for the family of T methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the MT method and the two-sided Taguchi (T1) method is for the family of T methods. In addition, the Ta and Tb methods, which are the improved versions of the T1 method, are included.
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2023-06-16 |
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r-mppa
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A procedure to test for dependence between point processes on the real line, e.g. causal dependence, correlation, inhibition or anti-correlation. The package also provides a number of utilities for plotting simultaneous point processes, and combining p-values.
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2023-06-16 |
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r-moments
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Functions to calculate: moments, Pearson's kurtosis, Geary's kurtosis and skewness; tests related to them (Anscombe-Glynn, D'Agostino, Bonett-Seier).
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2023-06-16 |
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r-modcmfitr
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Fits a modified version of the Connor-Mosimann distribution (Connor & Mosimann (1969)<doi:10.2307/2283728>), a Connor-Mosimann distribution or Dirichlet distribution (e.g. Gelman, Carlin, Stern & Rubin Chapter 3.5 (2004, <ISBN:1-58488-388-X>) to elicited quantiles of a multinomial distribution. Code is also provided to sample from the distributions, generating inputs suitable for a probabilistic sensitivity analysis / Monte Carlo simulation in a decision model.
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2023-06-16 |
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r-mmpf
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Marginalizes prediction functions using Monte-Carlo integration and computes permutation importance.
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2023-06-16 |
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r-mize
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Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems.
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2023-06-16 |
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r-mitools
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Tools to perform analyses and combine results from multiple-imputation datasets.
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2023-06-16 |
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r-mist
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Test for association between a set of SNPS/genes and continuous or binary outcomes by including variant characteristic information and using (weighted) score statistics.
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2023-06-16 |
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r-micromapst
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Provides the users with the ability to quickly create Linked Micromap plots for a collection of geographic areas. Linked Micromaps are visualizations of georeferenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the micromapST function. Contained in this package are border group datasets to support creating micromaps for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create micromaps for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package. Copyrighted 2013, 2014, 2015 and 2016 by Carr, Pearson and Pickle.
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2023-06-16 |
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r-mf
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Calculate MF (mitigated fraction) with clustering and bootstrap options. See http://goo.gl/pcXYVr for definition of MF.
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2023-06-16 |
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r-metrology
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Provides classes and calculation and plotting functions for metrology applications, including measurement uncertainty estimation and inter-laboratory metrology comparison studies.
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2023-06-16 |
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r-metricsweighted
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Provides weighted versions of several metrics, scoring functions and performance measures used in machine learning, including average unit deviances of the Bernoulli, Tweedie, Poisson, and Gamma distributions, see Jorgensen B. (1997, ISBN: 978-0412997112). The package also contains a weighted version of generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore, 'dplyr' chains are supported.
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2023-06-16 |
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r-meditations
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Prints a random quote from Marcus Aurelius' book Meditations.
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2023-06-16 |
