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r-sdmvspecies
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A software package help user to create virtual species for species distribution modelling. It includes several methods to help user to create virtual species distribution map. Those maps can be used for Species Distribution Modelling (SDM) study. SDM use environmental data for sites of occurrence of a species to predict all the sites where the environmental conditions are suitable for the species to persist, and may be expected to occur.
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2023-06-16 |
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r-scripturs
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Full text, in data frames containing one row per verse, of the Standard Works of The Church of Jesus Christ of Latter-day Saints (LDS). These are the Old Testament, (KJV), the New Testament (KJV), the Book of Mormon, the Doctrine and Covenants, and the Pearl of Great Price.
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2023-06-16 |
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r-scraper
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Tools for Scraping Data from Web-Based Documents
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2023-06-16 |
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r-scrabblescore
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Given a word will produce that word's scrabble score. Unlike many naive implementations this package takes into consideration the distribution of letter in scrabble. So a word like 'zzz' will be scored '10' rather than '30'.
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2023-06-16 |
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r-scperf
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Implements different inventory models, the bullwhip effect and other supply chain performance variables. Marchena Marlene (2010) <arXiv:1009.3977>.
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2023-06-16 |
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r-saic
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Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
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2023-06-16 |
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r-splitstackshape
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Online data collection tools like Google Forms often export multiple-response questions with data concatenated in cells. The concat.split (cSplit) family of functions splits such data into separate cells. The package also includes functions to stack groups of columns and to reshape wide data, even when the data are "unbalanced"---something which reshape (from base R) does not handle, and which melt and dcast from reshape2 do not easily handle.
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2023-06-16 |
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r-spatest
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Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>.
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2023-06-16 |
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r-sparktex
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Generate syntax for use with the sparklines package for LaTeX.
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2023-06-16 |
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r-snow
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Support for simple parallel computing in R.
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2023-06-16 |
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r-smallarea
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Inference techniques for Fay Herriot Model.
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2023-06-16 |
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r-sleepr
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Use behavioural variables to score activity and infer sleep from bouts of immobility. It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data. It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>), activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) and a new algorithm to detect when animals are dead.
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2023-06-16 |
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r-simplegraph
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Simple classic graph algorithms for simple graph classes. Graphs may possess vertex and edge attributes. 'simplegraph' has so dependencies and it is written entirely in R, so it is easy to install.
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2023-06-16 |
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r-simhaz
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Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
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2023-06-16 |
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r-sigora
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Pathway Analysis is the process of statistically linking observations on the molecular level to biological processes or pathways on the systems(i.e. organism, organ, tissue, cell) level. Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative. This can lead to identification of spurious pathways as statistically significant, since components are often shared amongst pathways. SIGORA seeks to avoid this pitfall by focusing on genes or gene-pairs that are (as a combination) specific to a single pathway. In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. The current version allows for pathway analysis of human and mouse datasets and contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories as well as mechanisms for extracting GPS for user supplied repositories.
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2023-06-16 |
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r-shinymaterial
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Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.
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2023-06-16 |
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r-seqtest
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Sequential triangular test for the arithmetic mean in one- and two- samples, proportions in one- and two-samples, and the Pearson's correlation coefficient.
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2023-06-16 |
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r-seqdesign
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A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
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2023-06-16 |
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r-seqalloc
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Potential randomization schemes are prospectively evaluated when units are assigned to treatment arms upon entry into the experiment. The schemes are evaluated for balance on covariates and on predictability (i.e., how well could a site worker guess the treatment of the next unit enrolled).
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2023-06-16 |
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r-sciviews
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Functions to install SciViews additions to R, and more tools.
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2023-06-16 |
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r-scina
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An automatic cell type detection and assignment algorithm for single cell RNA-Seq and Cytof/FACS data. 'SCINA' is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of markers, such as genes and protein symbols that are highly or lowly expressed in each category. See Zhang Z, et al (2019) <doi:10.3390/genes10070531> for more details.
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2023-06-16 |
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r-schoenberg
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Functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.
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2023-06-16 |
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r-scdensity
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Implements methods for obtaining kernel density estimates subject to a variety of shape constraints (unimodality, bimodality, symmetry, tail monotonicity, bounds, and constraints on the number of inflection points). Enforcing constraints can eliminate unwanted waves or kinks in the estimate, which improves its subjective appearance and can also improve statistical performance. The main function scdensity() is very similar to the density() function in 'stats', allowing shape-restricted estimates to be obtained with little effort. The methods implemented in this package are described in Wolters and Braun (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) <http://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>. See the scdensity() help for for full citations.
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2023-06-16 |
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r-sbf
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Smooth Backfitting for additive models using Nadaraya-Watson estimator
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2023-06-16 |
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r-safd
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The aim of the package is to provide some basic functions for doing statistics with one dimensional Fuzzy Data (in the form of polygonal fuzzy numbers). In particular, the package contains functions for the basic operations on the class of fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) as well as for calculating (Bertoluzza) distance and sample variance. Moreover a function to simulate fuzzy random variables and bootstrap tests for the equality of means is included. Version 2.1 fixes some bugs of previous versions.
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2023-06-16 |
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r-saemix
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The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group (<http://group.monolix.org/>).
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2023-06-16 |
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r-spiritr
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Contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol Items for Interventional Trials) statement outlines recommendations for a minimum set of elements to be addressed in a clinical trial protocol. Also contains functions to create a xml document from the template and upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for trial registration.
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2023-06-16 |
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r-spedinstabr
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From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
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2023-06-16 |
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r-solarpos
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Calculation of solar zenith and azimuth angles.
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2023-06-16 |
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r-soar
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Allows objects to be stored on disc and automatically recalled into memory, as required, by delayed assignment.
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2023-06-16 |
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r-sleuth3
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Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning.
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2023-06-16 |
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r-sleekts
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Compute Time series Resistant Smooth 4253H, twice smoothing method.
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2023-06-16 |
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r-simsurv
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Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) <http://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
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2023-06-16 |
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r-simexaft
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Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
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2023-06-16 |
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r-sier
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Methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.
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2023-06-16 |
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r-seqmade
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A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
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2023-06-16 |
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r-seermapperwest
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Provides supplemental 2000 census tract boundaries for the 14 states without Seer Registries that are west of the Mississippi river for use with the 'SeerMapper' package. The data contained in this package is derived from U. S. Census data and is in the public domain.
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2023-06-16 |
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r-seeclickfixr
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Provides a wrapper to access data from the SeeClickFix web API for R. SeeClickFix is a central platform employed by many cities that allows citizens to request their city's services. This package creates several functions to work with all the built-in calls to the SeeClickFix API. Allows users to download service request data from numerous locations in easy-to-use dataframe format manipulable in standard R functions.
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2023-06-16 |
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r-scrt
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Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
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2023-06-16 |
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r-scam
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Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package 'mgcv' are used for setting up the model matrix, printing and plotting the results. Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.
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2023-06-16 |
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r-sbsdiff
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Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.
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2023-06-16 |
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r-sbgcop
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Estimation and inference for parameters in a Gaussian copula model, treating the univariate marginal distributions as nuisance parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. This package also provides a semiparametric imputation procedure for missing multivariate data.
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2023-06-16 |
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r-sanon
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There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
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2023-06-16 |
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r-samplesizecmh
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Calculates the power and sample size for Cochran-Mantel-Haenszel tests. There are also several helper functions for working with probability, odds, relative risk, and odds ratio values.
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2023-06-16 |
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r-squash
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Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
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2023-06-16 |
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r-spina
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Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.
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2023-06-16 |
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r-spdownscale
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Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.
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2023-06-16 |
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r-spcadjust
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Calibration of thresholds of control charts such as CUSUM charts based on past data, taking estimation error into account.
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2023-06-16 |
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r-sparsedc
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Implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.
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2023-06-16 |
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r-sparql
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Use SPARQL to pose SELECT or UPDATE queries to an end-point.
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2023-06-16 |
