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r-svdvisual
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Some visualization tools based on Singular Value Decomposition
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2025-04-22 |
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r-svapls
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Accurate identification of genes that are truly differentially expressed over two sample varieties, after adjusting for hidden subject-specific effects of residual heterogeneity.
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2025-04-22 |
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r-survtrunc
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Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. The estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. Both methods require that the survival and truncation times are quasi-independent. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2017) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>.
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2025-04-22 |
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r-survrm2adapt
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Performs the procedure proposed by Horiguchi et al. (2018) <doi:10.1002/sim.7661>. The method specifies a set of truncation time points tau's for calculating restricted mean survival times (RMST), performs testing for equality, and estimates the difference in RMST between two groups at the specified tau's. Multiplicity by specifying several tau's is taken into account in this procedure.
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2025-04-22 |
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r-survrm2
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Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
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2025-04-22 |
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r-survregcenscov
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The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions.
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2025-04-22 |
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r-survlong
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Kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates.
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2025-04-22 |
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r-survjamda.data
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Three breast cancer gene expression data sets that can be used for package 'survJamda'. This package contains the gene expression and phenotype data of GSE1992, GSE3143 and GSE4335.
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2025-04-22 |
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r-survgini
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The Gini concentration test for survival data is a nonparametric test based on the Gini index for testing the equality of two survival distributions from the point of view of concentration. The package compares different nonparametric tests (asymptotic Gini test, permutation Gini test, log-rank test, Gray-Tsiatis test and Wilcoxon test) and computes their p-values.
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2025-04-22 |
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r-surveyoutliers
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At present, the only functionality is the calculation of optimal one-sided winsorizing cutoffs. The main function is optimal.onesided.cutoff.bygroup. It calculates the optimal tuning parameter for one-sided winsorisation, and so calculates winsorised values for a variable of interest. See the help file for this function for more details and an example.
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2025-04-22 |
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r-surveyeditor
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Help generate slides for surveys or experiments. The resulted slides allow the subject to respond with the use of the mouse (usual keyboard input is replaced with clicking on a virtual keyboard on the slide). Subjects' responses are saved to the user- specified location in the form of R-readable text file. To allow flexibility, each function in this package generates a particular type of slides thus general R function writing skills are required to compile these edited slides.
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2025-04-22 |
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r-survexp.fr
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Relative survival, AER and SMR based on French death rates
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2025-04-22 |
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r-survbootoutliers
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Three new methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.
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2025-04-22 |
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r-survawkmt2
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Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.
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2025-04-22 |
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r-surtex
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suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.
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2025-04-22 |
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r-surrosurvroc
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Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.
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2025-04-22 |
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r-surrogatetest
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Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019). Using a Surrogate Marker for Early Testing of a Treatment Effect. Biometrics, In press.
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2025-04-22 |
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r-surrogateoutcome
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Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in "Assessing the Value of a Censored Surrogate Outcome" by Parast L, Tian L, and Cai T which is currently in press at Lifetime Data Analysis. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.
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2025-04-22 |
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r-support.bws2
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Provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores.
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2025-04-22 |
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r-support.bws
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Provides three basic functions that support an implementation of object case (Case 1) best-worst scaling: one for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; one for creating a data set suitable for analysis; and one for calculating count-based scores.
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2025-04-22 |
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r-superpc
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Supervised principal components for regression and survival analsysis. Especially useful for high-dimnesional data, including microarray data.
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2025-04-22 |
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r-supernova
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Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN:978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.
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2025-04-22 |
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r-supermds
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Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis 55(1): 789-801.
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2025-04-22 |
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r-superdiag
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A Comprehensive Test Suite for Markov Chain Nonconvergence.
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2025-04-22 |
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r-supcluster
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Clusters features under the assumption that each cluster has a random effect and there is an outcome variable that is related to the random effects by a linear regression. In this way the cluster analysis is ``supervised'' by the outcome variable. An alternate specification is that features in each cluster have the same compound symmetric normal distribution, and the conditional distribution of the outcome given the features has the same coefficient for each feature in a cluster.
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2025-04-22 |
