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r-ltable
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Constructs tables of counts and proportions out of data sets. It has simplified syntax appealing for novice and even for advanced user under time pressure. It is particularly suitable for exploratory data analysis or presentation to single out most appropriate pieces of tabulated information. The other important feature is possibility to insert tables to Excel and Word documents.
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2023-06-16 |
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r-lpm
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Apply Univariate Long Memory Models, Apply Multivariate Short Memory Models To Hydrological Dataset, Estimate Intensity Duration Frequency curve to rainfall series.
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2023-06-16 |
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r-logisticrr
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Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.
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2023-06-16 |
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r-lmridge
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Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc. REFERENCES i. Hoerl and Kennard (1970) <doi:10.2307/1267351> ii. Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006> iii. Imdadullah, Aslam, and Saima (2017) iv. Marquardt (1970) <doi:10.2307/1267205>.
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2023-06-16 |
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r-lisreltor
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This is an unofficial package aimed at automating the import of LISREL output in R. This package or its maintainer is not in any way affiliated with the creators of LISREL and SSI, Inc.
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2023-06-16 |
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r-likelihoodexplore
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Provides likelihood functions as defined by Fisher (1922) <doi:10.1098/rsta.1922.0009> and a function that creates likelihood functions from density functions. The functions are meant to aid in education of likelihood based methods.
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2023-06-16 |
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r-lightningr
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The purpose of this package is to enable usage of lightningviz server to be accessible from R. The server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package by itself cannot and will not create any visualizations.
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2023-06-16 |
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r-nofrills
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Provides a compact variation of the usual syntax of function declaration, in order to support tidyverse-style quasiquotation of a function's arguments and body.
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2023-06-16 |
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r-nistunits
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Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for SI (International System of Units) and non-SI units, plus unit conversions Based on the data from NIST (National Institute of Standards and Technology, USA)
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2023-06-16 |
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r-neuroblastoma
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Annotated neuroblastoma copy number profiles, a benchmark data set for change-point detection algorithms.
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2023-06-16 |
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r-neuralnet
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Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.
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2023-06-16 |
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r-netregr
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Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.
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2023-06-16 |
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r-ncg
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Computes the noncentral gamma function: pdf, cdf, quantile function and inverse for the noncentrality parameter.
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2023-06-16 |
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r-mvtsplot
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A function for plotting multivariate time series data
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2023-06-16 |
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r-mvprpb
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Computes orthant probabilities multivariate normal distribution.
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2023-06-16 |
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r-mvisage
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Pearson and Spearman correlation coefficients are commonly used to quantify the strength of bivariate associations of genomic variables. For example, correlations of gene-level DNA copy number and gene expression measurements may be used to assess the impact of DNA copy number changes on gene expression in tumor tissue. 'MVisAGe' enables users to quickly compute and visualize the correlations in order to assess the effect of regional genomic events such as changes in DNA copy number or DNA methylation level. Please see Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research <doi:10.1158/0008-5472.CAN-17-3464>.
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2023-06-16 |
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r-multirng
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Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.
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2023-06-16 |
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r-mseapca
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This package provides functions for metabolite set enrichment analysis (MSEA) and principal component analysis (PCA), and converting metabolite set list from your own csv files or KEGG's tar.gz files to XML documents. This package is suitable for computation of MSEA for factor loading in PCA.
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2023-06-16 |
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r-mratios
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Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.
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2023-06-16 |
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r-mps
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Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.
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2023-06-16 |
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r-mpn
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Calculates the Most Probable Number (MPN) to quantify the concentration (density) of microbes in serial dilutions of a laboratory sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>). Also calculates the Aerobic Plate Count (APC) for similar microbial enumeration experiments.
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2023-06-16 |
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r-mpci
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It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
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2023-06-16 |
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r-modelobj
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A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions. In this way, a user can easily go from linear to non-linear models within the same package.
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-mknapsack
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Package solves multiple knapsack optimisation problem. Given a set of items, each with volume and value, it will allocate them to knapsacks of a given size in a way that value of top N knapsacks is as large as possible.
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2023-06-16 |
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r-mixphm
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Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
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2023-06-16 |
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r-mixmap
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A collection of functions to implement the MixMAP algorithm, which performs gene level tests of association using data from a previous GWAS or data from a meta-analysis of several GWAS. Conceptually, genes are detected as significant if the collection of p-values within a gene are determined to be collectively smaller than would be observed by chance.
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2023-06-16 |
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r-misreport
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Enables investigation of the predictors of misreporting on sensitive survey questions through a multivariate list experiment regression method. The method permits researchers to model whether a survey respondent's answer to the sensitive item in a list experiment is different from his or her answer to an analogous direct question.
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2023-06-16 |
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r-mindonstats
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66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
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2023-06-16 |
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r-mfag
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Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
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2023-06-16 |
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r-mdscore
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A set of functions to obtain modified score test for generalized linear models.
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2023-06-16 |
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r-mcpmodgeneral
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Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available. Bretz, F., Pinheiro, J. C., and Branson, M. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>. Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) <doi:10.2307/2533961>. Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) <doi:10.1002/sim.6052>.
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2023-06-16 |
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r-mbi
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Over 20 multiple-site diversity indices can be calculated. Later versions will include phylogenetic diversity
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2023-06-16 |
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r-mbc
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Calibrate and apply multivariate bias correction algorithms for climate model simulations of multiple climate variables. Three methods described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented: (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr), and (iii) MBC N-dimensional PDF transform (MBCn).
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2023-06-16 |
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r-matlabr
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Provides users to call MATLAB from using the "system" command. Allows users to submit lines of code or MATLAB m files. This is in comparison to 'R.matlab', which creates a MATLAB server.
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2023-06-16 |
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r-matchbook
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Provides a wrapper for the some basic functionality around the 'Matchbook' <http://www.matchbook.com> REST API. It features calls to get events, markets and runners in data frame format. It features functions for bet placement and position management and also allows reporting of settled bet transactions. Note: this package uses the back-lay format. The default for odds type, currency and language are set according to those of the registered user account.
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2023-06-16 |
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r-makefiler
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A user-friendly interface for the construction of 'Makefiles'.
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2023-06-16 |
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r-maditr
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Package provides pipe-style interface for 'data.table'. It preserves all 'data.table' features without significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data manipulation tasks. For example, you can write 'mtcars %>% take(mean(mpg), by = am)' for aggregation or 'mtcars %>% let(hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional aggregation/modification. 'query_if' function translates its arguments one-to-one to '[.data.table' method. Additionally there are some conveniences such as automatic 'data.frame' conversion to 'data.table'.
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2023-06-16 |
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r-lsr
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A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.
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2023-06-16 |
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r-lshorth
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Calculates the (localised) length of the shorth and supplies corresponding diagnostic plots.
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2023-06-16 |
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r-lrequire
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In the fashion of 'node.js' <https://nodejs.org/>, requires a file, sourcing into the current environment only the variables explicitly specified in the module.exports or exports list variable. If the file was already sourced, the result of the earlier sourcing is returned to the caller.
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2023-06-16 |
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r-lognormreg
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Functions to fits simple linear regression models with log normal errors and identity link (taking the responses on the original scale). See Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.
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2023-06-16 |
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r-logcondiscr
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Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
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2023-06-16 |
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r-lodi
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Impute observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI) in single-pollutant models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>. CLMI handles exposure detection limits that may change throughout the course of exposure assessment. 'lodi' provides functions for imputing and pooling for this method.
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2023-06-16 |
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r-lmmot
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Fit right censored Multiple Ordinal Tobit (MOT) model.
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2023-06-16 |
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r-lmesplines
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Add smoothing spline modelling capability to nlme. Fit smoothing spline terms in Gaussian linear and nonlinear mixed-effects models
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2023-06-16 |
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r-nnmis
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Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.
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2023-06-16 |
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r-nmw
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This shows how NONMEM(R) <http://www.iconplc.com/innovation/nonmem/> software works. NONMEM's classical estimation methods like 'First Order(FO) approximation', 'First Order Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.
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2023-06-16 |
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r-nicherover
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This package uses a probabilistic method to calculate niche regions and pairwise niche overlap using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. A forthcoming article by Swanson et al. (Ecology, 2014) provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data.
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2023-06-16 |
