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r-smd
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Computes standardized mean differences and confidence intervals for multiple data types based on Yang, D., & Dalton, J. E. (2012) <http://www.lerner.ccf.org/qhs/software/lib/stddiff.pdf>.
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2024-01-16 |
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r-smoothmest
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Some M-estimators for 1-dimensional location (Bisquare, ML for the Cauchy distribution, and the estimators from application of the smoothing principle introduced in Hampel, Hennig and Ronchetti (2011) to the above, the Huber M-estimator, and the median, main function is smoothm), and Pitman estimator.
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2024-01-16 |
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r-smoothie
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Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).
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2024-01-16 |
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r-smoothhr
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Provides flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are ex pressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived.
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2024-01-16 |
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r-smoother
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A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.
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2024-01-16 |
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r-smloutliers
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Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
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2024-01-16 |
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r-smirnov
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This tiny package contains one function smirnov() which calculates two scaled taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of originality). These two characteristics may be used for the analysis of similarities between any number of taxonomic groups, and also for assessing uniqueness of giving taxon. It is possible to use smirnov() output as a distance measure: convert it to distance by "as.dist(1 - smirnov(x))".
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2024-01-16 |
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r-smfilter
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Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.
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2024-01-16 |
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r-smarteda
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Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
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2024-01-16 |
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r-smatr
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Methods for fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples. See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.
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2024-01-16 |
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r-smdata
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Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
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2024-01-16 |
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r-smbinning
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A set of functions to build a scoring model from beginning to end, leading the user to follow an efficient and organized development process, reducing significantly the time spent on data exploration, variable selection, feature engineering, binning and model selection among other recurrent tasks. The package also incorporates monotonic and customized binning, scaling capabilities that transforms logistic coefficients into points for a better business understanding and calculates and visualizes classic performance metrics of a classification model.
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2024-01-16 |
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r-smcure
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An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models.
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2024-01-16 |
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r-smcrm
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Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York.
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2024-01-16 |
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r-smcfcs
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Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.
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2024-01-16 |
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r-slackr
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'Slack' <https://slack.com/> provides a service for teams to collaborate by sharing messages, images, links, files and more. Functions are provided that make it possible to interact with the 'Slack' platform 'API'. When you need to share information or data from R, rather than resort to copy/ paste in e-mails or other services like 'Skype' <https://www.skype.com/en/>, you can use this package to send well-formatted output from multiple R objects and expressions to all teammates at the same time with little effort. You can also send images from the current graphics device, R objects, and upload files.
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2024-01-16 |
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r-smartsizer
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A set of tools for determining the necessary sample size in order to identify the optimal dynamic treatment regime in a sequential, multiple assignment, randomized trial (SMART). Utilizes multiple comparisons with the best methodology to adjust for multiple comparisons. Designed for an arbitrary SMART design. Please see Artman (2018) <doi:10.1093/biostatistics/kxy064> for more details.
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2024-01-16 |
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r-smallarea
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Inference techniques for Fay Herriot Model.
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2024-01-16 |
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r-slouch
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An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.
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2024-01-16 |
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r-slippymath
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Provides functions for performing common tasks when working with slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen, among others. Functionality includes converting from latitude and longitude to tile numbers, determining tile bounding boxes, and compositing tiles to a georeferenced raster image.
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2024-01-16 |
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r-slim
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Fits singular linear models to longitudinal data. Singular linear models are useful when the number, or timing, of longitudinal observations may be informative about the observations themselves. They are described in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general longitudinal data.
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2024-01-16 |
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r-slideview
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Create a side-by-side view of raster(image)s with an interactive slider to switch between regions of the images. This can be especially useful for image comparison of the same region at different time stamps.
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2024-01-16 |
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r-slickr
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Create and customize interactive carousels using the 'Slick' 'JavaScript' library and the 'htmlwidgets' package. The carousels can contain plots produced in R, images, 'iframes', videos and other 'htmlwidgets'. These carousels can be created directly from the R console, and viewed in the 'RStudio' internal viewer, in 'Shiny' apps and R Markdown documents.
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2024-01-16 |
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r-skimr
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A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.
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2024-01-16 |
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r-slide
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This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
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2024-01-16 |
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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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2024-01-16 |
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r-sleuth2
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Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury.
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2024-01-16 |
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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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2024-01-16 |
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r-skewhyperbolic
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Functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. There are also functions that fit the distribution to data. There are functions for the mean, variance, skewness, kurtosis and mode of a given distribution and to calculate moments of any order about any centre. To assess goodness of fit, there are functions to generate a Q-Q plot, a P-P plot and a tail plot.
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2024-01-16 |
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r-sleekts
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Compute Time series Resistant Smooth 4253H, twice smoothing method.
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2024-01-16 |
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r-sldassay
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Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.
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2024-01-16 |
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r-skmeans
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Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a fixed-point algorithm and an interface to the CLUTO vcluster program.
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2024-01-16 |
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r-sjstats
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Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), or for which currently no functions available. Second, another focus lies on weighted variants of common statistical measures and tests like weighted standard error, mean, t-test, correlation, and more.
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2024-01-16 |
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r-skedastic
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Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) <https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) <https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979) <doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, Yuce (2008) <https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <arXiv:0810.2124v1>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) <doi:10.2307/1912934>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016) <doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017) <doi:10.1080/00949655.2016.1198906>.
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2024-01-16 |
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r-skewt
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Density, distribution function, quantile function and random generation for the skewed t distribution of Fernandez and Steel.
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2024-01-16 |
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r-sjplot
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Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.
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2024-01-16 |
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r-skellam
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Functions for the Skellam distribution, including: density (pmf), cdf, quantiles and regression.
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2024-01-16 |
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r-skeletor
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A tool for bootstrapping new packages with useful defaults, including a test suite outline that passes checks and helpers for running tests, checking test coverage, building vignettes, and more. Package skeletons it creates are set up for pushing your package to 'GitHub' and using other hosted services for building and test automation.
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2024-01-16 |
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r-sjlabelled
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Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.
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2024-01-16 |
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r-sixsigma
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Functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" `[ISBN 978-1-4614-3652-2]` and "Quality Control with R" `[ISBN 978-3-319-24046-6]`, are also included in the package.
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2024-01-16 |
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r-sjmisc
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Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a 'tidyverse'-workflow.
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2024-01-16 |
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r-sis
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Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).
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2024-01-16 |
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r-sjdbc
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Provides a database-independent JDBC interface.
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2024-01-16 |
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r-singlecasees
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Provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions.
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2024-01-16 |
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r-sizer
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Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths.
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2024-01-16 |
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r-simr
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Calculate power for generalised linear mixed models, using simulation. Designed to work with models fit using the 'lme4' package. Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
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2024-01-16 |
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r-sitree
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Framework to build an individual tree simulator.
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2024-01-16 |
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r-sitools
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Format a number (or a list of numbers) to a string (or a list of strings) with SI prefix. Use SI prefixes as constants like (4 * milli)^2
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2024-01-16 |
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r-sisvive
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Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
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2024-01-16 |
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r-sirad
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Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
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2024-01-16 |
