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r-lcpm
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An implementation of the Log Cumulative Probability Model (LCPM) and Proportional Probability Model (PPM) for which the Maximum Likelihood Estimates are determined using constrained optimization. This implementation accounts for the implicit constraints on the parameter space. Other features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization.
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2023-06-16 |
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r-nlsylinks
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Utilities and kinship information for behavior genetics and developmental research using the National Longitudinal Survey of Youth (NLSY; <http://www.bls.gov/nls/>).
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2023-06-16 |
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r-nlmrt
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Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.
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2023-06-16 |
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r-nhanes
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Body Shape and related measurements from the US National Health and Nutrition Examination Survey (NHANES, 1999-2004). See http://www.cdc.gov/nchs/nhanes.htm for details.
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2023-06-16 |
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r-nfwdist
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Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.
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2023-06-16 |
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r-ncopula
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Construct and manipulate hierarchical Archimedean copulas with multivariate compound distributions. The model used is the one of Cossette et al. (2017) <doi:10.1016/j.insmatheco.2017.06.001>.
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2023-06-16 |
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r-murphydiagram
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Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings', 2015).
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2023-06-16 |
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r-multiplex
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Algebraic procedures for the analysis of multiple social networks are delivered with this package. Among other things, it makes possible to create and manipulate multivariate network data with different formats, and there are effective ways available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup or the semiring structure together with the relational bundles occurring in different types of multivariate network data sets. It also provides an algebraic approach for two-mode networks through Galois derivations between families of the pairs of subsets in the two domains.
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2023-06-16 |
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r-multinomialci
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An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial distribution given a set of observations, proposed by Sison and Glaz in their paper: Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). The method is an R translation of the SAS code implemented by May and Johnson in their paper: May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). Paper and code available at <DOI:10.18637/jss.v005.i06>.
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2023-06-16 |
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r-multinbmod
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This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
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2023-06-16 |
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r-multilaterals
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Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.
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2023-06-16 |
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r-msu
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Estimators for multivariate symmetrical uncertainty based on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>, total correlation, information gain and symmetrical uncertainty of categorical variables.
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2023-06-16 |
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r-msg
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A companion to the Chinese book ``Modern Statistical Graphics''.
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2023-06-16 |
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r-mpe
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Functions for calculating sample size and power for clinical trials with multiple (co-)primary endpoints.
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2023-06-16 |
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r-morgenstemning
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This package is a port of the MATLAB colourmap functions accompanying the paper M. Geissbuehler and T. Lasser, "How to display data by color schemes compatible with red-green color perception deficiencies," Opt. Express 21, 9862-9874 (2013) to R.
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2023-06-16 |
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r-morder
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MOrder provide functions to check time homogeneity and order of markov chain by using chi-squared test, AIC value and BIC value.
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2023-06-16 |
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r-mondate
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Keep track of dates in terms of months. Model dates as at close of business. Perform date arithmetic in units of "months" and "years" (multiples of months). Allow "infinite" dates to model "ultimate" time spans.
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2023-06-16 |
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r-mmwrweek
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The first day of any MMWR week is Sunday. MMWR week numbering is sequential beginning with 1 and incrementing with each week to a maximum of 52 or 53. MMWR week #1 of an MMWR year is the first week of the year that has at least four days in the calendar year. This package provides functionality to convert Dates to MMWR day, week, and year and the reverse.
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2023-06-16 |
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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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2023-06-16 |
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r-mixreg
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Fits mixtures of (possibly multivariate) regressions (which has been described as doing ANCOVA when you don't know the levels).
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2023-06-16 |
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r-mittagleffler
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Implements the Mittag-Leffler function, distribution, random variate generation, and estimation. Based on the Laplace-Inversion algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.
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2023-06-16 |
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r-minxent
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This package implements entropy optimization distribution under specified constraints. It also offers an R interface to the MinxEnt and MaxEnt distributions.
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2023-06-16 |
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r-microbats
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A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
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2023-06-16 |
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r-mhtdiscrete
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A comprehensive tool for almost all existing multiple testing methods for discrete data. The package also provides some novel multiple testing procedures controlling FWER/FDR for discrete data. Given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha and make decisions. For users' convenience, the functions also provide the output option for printing decision rules.
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2023-06-16 |
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r-metricsgraphics
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Provides an 'htmlwidgets' interface to the 'MetricsGraphics.js' ('D3'-based) charting library which is geared towards displaying time-series data. Chart types include line charts, scatterplots, histograms and rudimentary bar charts. Support for laying out multiple charts into a grid layout is also provided. All charts are interactive and many have an option for line, label and region annotations.
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2023-06-16 |
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r-metafor
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A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.
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2023-06-16 |
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r-meta4diag
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Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <http://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).
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2023-06-16 |
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r-mefa
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A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
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2023-06-16 |
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r-mdendro
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A collection of methods for agglomerative hierarchical clustering strategies on a matrix of distances, implemented using the variable-group approach introduced in Fernandez and Gomez (2008) <doi:10.1007/s00357-008-9004-x>. Descriptive measures to analyze the resulting hierarchical trees are also provided. In addition to the usual clustering methods, two parameterized methods are provided to explore an infinite family of hierarchical clustering strategies. When there are ties in proximity values, the hierarchical trees obtained are unique and independent of the order of the elements in the input matrix.
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2023-06-16 |
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r-mcbftest
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Monte Carol based tests for the Behrens Fisher Problem enhance the statistical power and performs better than Welch's t-approximation, see Ullah et al. (2019).
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2023-06-16 |
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r-masae
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An S4 implementation of the unbiased extension of the model- assisted synthetic-regression estimator proposed by Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013) <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>. It yields smaller variances than the standard bias correction, the generalised regression estimator.
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2023-06-16 |
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r-makeproject
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This package creates an empty framework of files and directories for the "Load, Clean, Func, Do" structure described by Josh Reich.
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2023-06-16 |
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r-lunar
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Provides functions to calculate lunar and other environmental covariates.
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2023-06-16 |
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r-lpc
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Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
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2023-06-16 |
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r-lpbrim
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Optimization of bipartite modularity using LP-BRIM (Label propagation followed by Bipartite Recursively Induced Modularity).
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2023-06-16 |
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r-loon.data
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Data used as examples in the 'loon' package.
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2023-06-16 |
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r-lognorm
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The lognormal distribution (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>) can characterize uncertainty that is bounded by zero. This package provides estimation of distribution parameters, computation of moments and other basic statistics, and an approximation of the distribution of the sum of several correlated lognormally distributed variables (Lo 2013 <doi:10.12988/ams.2013.39511>).
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2023-06-16 |
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r-lnirt
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Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, <doi:10.18637/jss.v020.i07>.
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2023-06-16 |
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r-lmec
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This package includes a function to fit a linear mixed-effects model in the formulation described in Laird and Ware (1982) but allowing for censored normal responses. In this version, the with-in group errors are assumed independent and identically distributed.
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2023-06-16 |
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r-liureg
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Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc. REFERENCES i. Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585> ii. Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011> iii. Imdadullah, Aslam, and Saima (2017) iv. Liu (1993) <doi:10.1080/03610929308831027> v. Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.
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2023-06-16 |
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r-linearregressionmde
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Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
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2023-06-16 |
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r-limitplot
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Values below a specified limit of detection are stacked in rows in order to reduce overplotting and create a clear graphical representation of your data.
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2023-06-16 |
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r-lhmixr
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Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.
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2023-06-16 |
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r-learnrbook
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Datasets used in the book "Learn R ...as you learnt your mother tongue" by Pedro J. Aphalo (2017) <https://leanpub.com/learnr>.
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2023-06-16 |
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r-learnr
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Create interactive tutorials using R Markdown. Use a combination of narrative, figures, videos, exercises, and quizzes to create self-paced tutorials for learning about R and R packages.
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2023-06-16 |
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r-ldbounds
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Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach.
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2023-06-16 |
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r-nlmeu
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nlmeU: Datasets and utility functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models: A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.
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2023-06-16 |
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r-nlar
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Client for programmatic access to the 2007 and 2012 National Lakes Assessment database <https://www.epa.gov/national-aquatic-resource-surveys/nla> containing data for hundreds of lakes in the lower 48 states of the contiguous US.
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2023-06-16 |
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r-newtestsurvrec
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Implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a model for recurrent event, when the data are correlated or not correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.
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2023-06-16 |
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r-neural
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RBF and MLP neural networks with graphical user interface
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2023-06-16 |
