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r-string2adjmatrix
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Takes a list of character strings and forms an adjacency matrix for the times the specified characters appear together in the strings provided. For use in social network analysis and data wrangling. Simple package, comprised of three functions.
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2025-04-22 |
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r-stressstrength
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Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.
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2025-04-22 |
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r-stressr
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Forms queries to submit to the Cleveland Federal Reserve Bank web site's financial stress index data site. Provides query functions for both the composite stress index and the components data. By default the download includes daily time series data starting September 25, 1991. The functions return a class of either type easing or cfsi which contain a list of items related to the query and its graphical presentation. The list includes the time series data as an xts object. The package provides four lattice time series plots to render the time series data in a manner similar to the bank's own presentation.
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2025-04-22 |
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r-streammetabolism
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I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.
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2025-04-22 |
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r-stratifiedbalancing
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Performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.
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2025-04-22 |
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r-strategy
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Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches. The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies. Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.
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2025-04-22 |
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r-storr
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Creates and manages simple key-value stores. These can use a variety of approaches for storing the data. This package implements the base methods and support for file system, in-memory and DBI-based database stores.
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2025-04-22 |
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r-stopes
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A variable selection procedure for low to moderate size linear regressions models. This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model.
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2025-04-22 |
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r-stoichcalc
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Given a list of substance compositions, a list of substances involved in a process, and a list of constraints in addition to mass conservation of elementary constituents, the package contains functions to build the substance composition matrix, to analyze the uniqueness of process stoichiometry, and to calculate stoichiometric coefficients if process stoichiometry is unique. (See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.)
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2025-04-22 |
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r-stockchina
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With this package, users can obtain the real-time price and volume information of stocks in China market, as well as the information of the stock index. This package adopted the API from Sina Finance (http://finance.sina.com.cn/).
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2025-04-22 |
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r-stochprofml
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This is an R package accompanying the paper "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes (PNAS 2014, 111(5), E626-635). In this paper, we measure expression profiles from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and the parameters of these lognormal distributions from the cumulated expression measurements.
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2025-04-22 |
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r-stmgp
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Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.
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2025-04-22 |
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r-stinepack
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A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.
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2025-04-22 |
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r-sticky
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In base R, object attributes are lost when objects are modified by common data operations such as subset, filter, slice, append, extract etc. This packages allows objects to be marked as 'sticky' and have attributes persisted during these operations or when inserted into or extracted from recursive (i.e. list- or table-like) objects.
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2025-04-22 |
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r-stheoreme
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Functions implementing the procedure of entropy comparison between two data samples after the renormalization of respective probability distributions with the algorithm designed by Klimontovich (Zeitschrift fur Physik B Condensed Matter. 1987, Volume 66, Issue 1, pp 125-127) and extended by Anishchenko (Proc. SPIE 2098, Computer Simulation in Nonlinear Optics. 1994, pp.130-136). The package also includes data preparation tools which can also be used separately for various applications.
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2025-04-22 |
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r-stevedore
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Work with containers over the Docker API. Rather than using system calls to interact with a docker client, using the API directly means that we can receive richer information from docker. The interface in the package is automatically generated using the 'OpenAPI' (a.k.a., 'swagger') specification, and all return values are checked in order to make them type stable.
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2025-04-22 |
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r-stemmatology
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Explore and analyse the genealogy of textual or musical traditions, from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by Camps and Cafiero (2015) <doi:10.1484/M.LECTIO-EB.5.102565>.
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2025-04-22 |
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r-stem
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Estimation of the parameters of a spatio-temporal model using the EM algorithm, estimation of the parameter standard errors using a spatio-temporal parametric bootstrap, spatial mapping.
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2025-04-22 |
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r-stellar
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A package to manage and display stellar tracks and isochrones from Pisa low-mass database. Includes tools for isochrones construction and tracks interpolation.
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2025-04-22 |
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r-steiniv
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Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004) <DOI:10.1198/016214504000000430>.
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2025-04-22 |
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r-steinernet
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A set of functions for finding and analysing Steiner trees. It has applications in biological pathway network analysis. Sadeghi (2013) <doi:10.1186/1471-2105-14-144>.
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2025-04-22 |
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r-stddiff
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Contains three main functions including stddiff.numeric(), stddiff.binary() and stddiff.category(). These are used to calculate the standardized difference between two groups. It is especially used to evaluate the balance between two groups before and after propensity score matching.
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2025-04-22 |
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r-stcov
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Estimates a covariance matrix using Stein's isotonized covariance estimator, or a related estimator suggested by Haff.
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2025-04-22 |
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r-statprograms
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A small collection of data on graduate statistics programs from the United States.
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2025-04-22 |
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r-statpermeco
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Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>, Amendola et al. (2015) <doi:10.1002/for.2322> and Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.
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2025-04-22 |
