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r-noaaoceans
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Provides a small set of tools for collecting data from National Oceanic and Atmospheric Administration (NOAA) data sources. The functions provided in the package are wrappers around NOAA's existing APIs which is found at <https://tidesandcurrents.noaa.gov/api/>.
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2023-06-16 |
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r-nmof
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Functions, examples and data from the book "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2011), ISBN 978-0123756626. The package provides implementations of several optimisation heuristics, such as Differential Evolution, Genetic Algorithms and Threshold Accepting. There are also functions for the valuation of financial instruments, such as bonds and options, and functions that help with stochastic simulations.
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2023-06-16 |
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r-netweaver
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Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
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2023-06-16 |
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r-naturalsort
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Provides functions related to human natural ordering. It handles adjacent digits in a character sequence as a number so that natural sort function arranges a character vector by their numbers, not digit characters. It is typically seen when operating systems lists file names. For example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1 < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth characters.
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2023-06-16 |
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r-mvnbayesian
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Tools of Bayesian analysis framework using the method suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.
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2023-06-16 |
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r-multirobust
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Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.
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2023-06-16 |
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r-multijoin
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Wrapper around the Unix join facility which is more efficient than the built-in R routine merge(). The package enables the joining of multiple files on disk at once. The files can be compressed and various filters can be deployed before joining. Compiles only under Unix.
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2023-06-16 |
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r-most
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Provides functions similar to the 'SAS' macros previously provided to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826> and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers which outline practical benefits and challenges of factorial and fractional factorial experiments for scientists interested in developing biological and/or behavioral interventions, especially in the context of the multiphase optimization strategy (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>. The package currently contains three functions. First, RelativeCosts1() draws a graph of the relative cost of complete and reduced factorial designs versus other alternatives. Second, RandomAssignmentGenerator() returns a dataframe which contains a list of random numbers that can be used to conveniently assign participants to conditions in an experiment with many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect size, or required sample size of a factorial or fractional factorial experiment, for main effects or interactions, given several possible choices of effect size metric, and allowing pretests and clustering.
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2023-06-16 |
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r-mole
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Model for simulating language evolution in terms of cultural evolution (Smith & Kirby (2008) <DOI:10.1098/rstb.2008.0145>; Deacon 1997). The focus is on the emergence of argument-marking systems (Dowty (1991) <DOI:10.1353/lan.1991.0021>, Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) <DOI:10.1075/sl.21.2.04ari>, Lestrade (2010) <DOI:10.7282/T3ZG6R4S>), person indexing (Ariel 1999, Dahl (2000) <DOI:10.1075/fol.7.1.03dah>, Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) <DOI:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015) <DOI:10.1002/9781118346136.ch27>) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) <DOI:10.3765/bls.v13i0.1834>, Givon (1995) <DOI:10.1075/z.74>, Croft (2000), Saffran (2001) <DOI:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) <DOI:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). In Lestrade (2015b) <DOI:10.15496/publikation-8640>, Lestrade (2015c) <DOI:10.1075/avt.32.08les>, and Lestrade (2016) <DOI:10.17617/2.2248195>), which reported on the results of preliminary versions, this package was announced as WDWTW (for who does what to whom), but for reasons of pronunciation and generalization the title was changed.
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2023-06-16 |
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r-modifiedmk
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Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size. Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>. Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. sen, P. K. (1968).<doi:10.2307/2285891>. Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>. Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>. Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>. Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>. Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.
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2023-06-16 |
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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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2023-06-16 |
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r-mleur
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Provides functions for unit root testing using MLE method
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2023-06-16 |
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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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2023-06-16 |
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r-mixsmsn
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Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions.
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2023-06-16 |
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r-mixmeta
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A collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models.
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2023-06-16 |
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r-minigui
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Quick and simple Tcl/Tk Graphical User Interface to call functions. Also comprises a very simple experimental GUI framework.
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2023-06-16 |
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r-miceconindex
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Tools for calculating Laspeyres, Paasche, and Fisher price and quantity indices.
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2023-06-16 |
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r-mhtrajectoryr
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Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
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2023-06-16 |
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r-metnorm
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Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package contains a collection of R functions which can be used to remove unwanted variation and obtain normalized metabolomics data.
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2023-06-16 |
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r-merror
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N>=3 methods are used to measure each of n items. The data are used to estimate simultaneously systematic error (bias) and random error (imprecision). Observed measurements for each method or device are assumed to be linear functions of the unknown true values and the errors are assumed normally distributed. Maximum likelihood estimation is used for the imprecision standard deviation estimates. Pairwise calibration curves and plots can be easily generated.
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2023-06-16 |
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r-medicare
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Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.
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2023-06-16 |
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r-mcheatmaps
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mcheatmaps serves to visualize multiple different symmetric matrices and matrix clusters in a single figure using a dendogram, two half matrices and various color labels.
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2023-06-16 |
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r-mcbiopi
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Computes the prime implicants or a minimal disjunctive normal form for a logic expression presented by a truth table or a logic tree. Has been particularly developed for logic expressions resulting from a logic regression analysis, i.e. logic expressions typically consisting of up to 16 literals, where the prime implicants are typically composed of a maximum of 4 or 5 literals.
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2023-06-16 |
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r-matskew
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Performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017) <doi: 10.1002/sta4.143>.
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2023-06-16 |
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r-matlab
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Emulate MATLAB code using R
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2023-06-16 |
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r-masstimate
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Estimation equations are from a variety of sources but are, in general, based on regressions between skeletal measurements (e.g., femoral circumference) and body mass in living taxa.
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2023-06-16 |
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r-march
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Computation of various Markovian models for categorical data including homogeneous Markov chains of any order, MTD models, Hidden Markov models, and Double Chain Markov Models.
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2023-06-16 |
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r-mail
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Easy to use package for sending email notifications with status information from R
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2023-06-16 |
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r-lumberjack
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A framework that allows for easy logging of changes in data. Main features: start tracking changes by adding a single line of code to an existing script. Track changes in multiple datasets, using multiple loggers. Add custom-built loggers or use loggers offered by other packages.
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2023-06-16 |
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r-ltr
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A set of functions to execute the linear-transformation of replicate (LTR) algorithm for preprocessing of microarray data
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2023-06-16 |
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r-lsplsglm
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Fit logistic regression models using LS-PLS approaches to analyse both clinical and genomic data. (C. Bazzoli and S. Lambert-Lacroix. (2017) Classification using LS-PLS with logistic regression based on both clinical and gene expression variables <https://hal.archives-ouvertes.fr/hal-01405101>).
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2023-06-16 |
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r-lsa
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The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.
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2023-06-16 |
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r-loop
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The program can perform loop analysis and plot network structure (especially for food webs),including minimum spanning tree, loop decomposition of weighted directed graphs, and other network properties which may be related to food chain properties in ecology.
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2023-06-16 |
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r-longitudinal
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Contains general data structures and functions for longitudinal data with multiple variables, repeated measurements, and irregularly spaced time points. Also implements a shrinkage estimator of dynamical correlation and dynamical covariance.
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2023-06-16 |
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r-lmodel2
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Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA).
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2023-06-16 |
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r-lmfilter
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We present a method based on filtering algorithms to estimate the parameters of linear regressions, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods.
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2023-06-16 |
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r-likelihood
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Tools for maximum likelihood estimation of parameters of scientific models.
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2023-06-16 |
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r-lfactors
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Provides an extension to factors called 'lfactor' that are similar to factors but allows users to refer to 'lfactor' levels by either the level or the label.
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2023-06-16 |
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r-leanr
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Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) <DOI:10.1093/bioinformatics/btw676>] Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values. The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.
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2023-06-16 |
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r-nlgeocoder
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R interface to the open location server API of 'Publieke Diensten Op de Kaart' (<http://www.pdok.nl>). It offers geocoding, address suggestions and lookup of geographical objects. Included is an utility function for displaying leaflet tiles restricted to the Netherlands.
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2023-06-16 |
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r-nephro
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Set of functions to estimate renal function and other phenotypes of interest in nephrology based on different biomechimal traits. MDRD, CKD-EPI, and Virga equations are compared in Pattaro (2013) <doi:10.1159/000351043>, where the respective references are given. In addition, the software includes Stevens (2008) <doi:10.1053/j.ajkd.2007.11.018> and Cockroft (1976) <doi:10.1159/000180580> formulas.
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2023-06-16 |
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r-ndp
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An interactive presentation on the topic of normal distribution using 'rmarkdown' and 'shiny' packages. It is helpful to those who want to learn normal distribution quickly and get a hands on experience. The presentation has a template for solving problems on normal distribution. Runtime examples are provided in the package function as well as at <https://kartikeyastat.shinyapps.io/NormalDistribution/>.
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2023-06-16 |
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r-ncf
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R functions for analyzing spatial (cross-)covariance: the nonparametric (cross-)covariance function, the spline correlogram, the nonparametric phase coherence function, local indicators of spatial association (LISA), (Mantel) correlogram, (Partial) Mantel test.
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2023-06-16 |
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r-multivator
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A multivariate generalization of the emulator package.
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2023-06-16 |
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r-multigroup
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Several functions are presented in order to study data in a group structure, where the same set of variables are measured on different groups of individuals.
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2023-06-16 |
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r-modygliani
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RMSD and Internal Energy analysis of NAMD and YASARA Molecular Dynamics output files. Allows to comparison of different dynamics per different complexes. Input files have to be ASCII files tab separated.
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2023-06-16 |
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r-modeva
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Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.
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2023-06-16 |
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r-mixspe
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Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew power exponential and power exponential distributions, respectively. A generalized expectation-maximization framework is used for parameter estimation. Methodology for mixtures of power exponential distributions is from Dang et al. (2015) <doi: 10.1111/biom.12351>.
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2023-06-16 |
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r-mirsea
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The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
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2023-06-16 |
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r-minimax
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The minimax family of distributions is a two-parameter family like the beta family, but computationally a lot more tractible.
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2023-06-16 |
