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r-seqrflp
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This package includes functions for handling DNA sequences, especially simulated RFLP and TRFLP pattern based on selected restriction enzyme and DNA sequences.
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2025-03-25 |
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r-seqmon
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S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history.
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2025-03-25 |
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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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2025-03-25 |
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r-seqicp
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Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.
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2025-03-25 |
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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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2025-03-25 |
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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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2025-03-25 |
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r-separationplot
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Functions to generate separation plots for evaluation of model fit.
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2025-03-25 |
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r-separate
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It combines maximum likelihood estimation of the parameters of matrix and 3rd-order tensor normal distributions with unstructured factor variance-covariance matrices, two procedures, and unbiased modified likelihood ratio testing of simple and double separability for variance-covariance structures, two procedures.
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2025-03-25 |
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r-senstrat
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Sensitivity analysis in unmatched observational studies, with or without strata. The main functions are sen2sample() and senstrat(). See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .
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2025-03-25 |
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r-sensrivastava
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Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book.
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2025-03-25 |
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r-sensr
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Provides methods for sensory discrimination methods; duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes. Methods for profile likelihood confidence intervals and plotting are included.
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2025-03-25 |
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r-sensory
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Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
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2025-03-25 |
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r-sensmediation
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We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.
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2025-03-25 |
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r-sensitivitymw
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Sensitivity analysis analysis in matched observational studies with multiple controls using weighted M-statistics to increase design sensitivity.
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2025-03-25 |
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r-sensitivitymv
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The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.
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2025-03-25 |
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r-sensitivitymult
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Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>. Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
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2025-03-25 |
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r-sensitivityfull
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Sensitivity to unmeasured biases in an observational study that is a full match. Function senfm() performs tests and function senfmCI() creates confidence intervals. The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.
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2025-03-25 |
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r-sensitivitycasecontrol
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This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions.
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2025-03-25 |
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r-sensitivity2x2xk
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Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
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2025-03-25 |
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r-sensibo.sky
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Provides an interface to the 'Sensibo Sky' API which allows to remotely control non-smart air conditioning units. See <https://sensibo.com> for more informations.
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2025-03-25 |
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r-sensemakr
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Implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli and Hazlett (2018) <https://www.researchgate.net/publication/322509816_Making_Sense_of_Sensitivity_Extending_Omitted_Variable_Bias>.
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2025-03-25 |
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r-sendmailr
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Package contains a simple SMTP client which provides a portable solution for sending email, including attachment, from within R.
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2025-03-25 |
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r-sempower
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Provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM). Moshagen & Erdfelder (2016) <doi:10.1080/10705511.2014.950896>.
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2025-03-25 |
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r-sempls
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Fits structural equation models using partial least squares (PLS). The PLS approach is referred to as 'soft-modeling' technique requiring no distributional assumptions on the observed data.
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2025-03-25 |
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r-semnetdictionaries
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Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to <https://github.com/AlexChristensen/SemNetDictionaries>.
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2025-03-25 |
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r-semmodcomp
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Conduct tests of difference in fit for mean and covariance structure models as in structural equation modeling (SEM)
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2025-03-25 |
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r-semipar
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Functions for semiparametric regression analysis, to complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J. (2003). Semiparametric Regression. Cambridge University Press.
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2025-03-25 |
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r-seminr
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A powerful, easy to write and easy to modify syntax for specifying and estimating Partial Least Squares (PLS) path models allowing for the latest estimation methods for Consistent PLS as per Dijkstra & Henseler (2015, MISQ 39(2): 297-316), adjusted interactions as per Henseler & Chin (2010) <doi:10.1080/10705510903439003> and bootstrapping utilizing parallel processing as per Hair et al. (2017, ISBN:978-1483377445).
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2025-03-25 |
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r-semid
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Provides routines to check identifiability or non-identifiability of linear structural equation models as described in Drton, Foygel, and Sullivant (2011) <DOI:10.1214/10-AOS859>, Foygel, Draisma, and Drton (2012) <DOI:10.1214/12-AOS1012>, and other works. The routines are based on the graphical representation of structural equation models by a path diagram/mixed graph.
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2025-03-25 |
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r-selfingtree
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A probability tree allows to compute probabilities of complex events, such as genotype probabilities in intermediate generations of inbreeding through recurrent self-fertilization (selfing). This package implements functionality to compute probability trees for two- and three-marker genotypes in the F2 to F7 selfing generations. The conditional probabilities are derived automatically and in symbolic form. The package also provides functionality to extract and evaluate the relevant probabilities.
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2025-03-25 |
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r-seleniumpipes
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The W3C WebDriver specification defines a way for out-of-process programs to remotely instruct the behaviour of web browsers. It is detailed at <https://w3c.github.io/webdriver/webdriver-spec.html>. This package provides an R client implementing the W3C specification.
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2025-03-25 |
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r-selemix
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Detection of outliers and influential errors using a latent variable model.
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2025-03-25 |
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r-selectmeta
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Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
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2025-03-25 |
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r-selectiongain
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Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.
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2025-03-25 |
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r-selectapref
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Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion.Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies.
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2025-03-25 |
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r-sejong
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Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP
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2025-03-25 |
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r-seismic
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An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.
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2025-03-25 |
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r-segregation
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Computes entropy-based segregation indices, as developed by Theil (1971) <isbn:978-0471858454>, with a focus on the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>, is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. Includes standard error estimation by bootstrapping.
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2025-03-25 |
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r-segmented
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Given a regression model, segmented `updates' it by adding one or more segmented (i.e., piece-wise linear) relationships. Several variables with multiple breakpoints are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>).
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2025-03-25 |
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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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2025-03-25 |
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r-seermapperregs
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Provides supplemental 2000 census tract boundaries for the 15 states containing Seer Registries 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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2025-03-25 |
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r-seermappereast
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Provides supplemental 2000 census tract boundaries for the 23 states without Seer Registries that are east 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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2025-03-25 |
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r-seermapper2010west
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Provides supplemental 2010 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. 2010 Census data and is in public domain.
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2025-03-25 |
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r-seermapper2010regs
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Provides supplemental 2010 census tract boundaries of the 15 states containing Seer Registries for use with the 'SeerMapper' package. The data contained in this package is derived from U. S. 2010 Census data and is in public domain.
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2025-03-25 |
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r-seermapper2010east
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Provides supplemental 2010 census tract boundary package for 23 states without Seer Registries that are east 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 public domain.
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2025-03-25 |
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r-seer2r
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read and write SEER*STAT data files
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2025-03-25 |
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r-seedwater
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Bringing together tools for modeling drying and soaking (rehydration) kinetics of seeds. This package contains several widely used predictive models (e.g.: da Silva et al., 2018). As these are nonlinear, the functions are interactive-based and easy-to-use. Least squares estimates are obtained with just a few visual adjustments of the initial parameter values. Reference: da Silva AR et al. (2018) <doi:10.2134/agronj2017.07.0373>.
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2025-03-25 |
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r-seedcalc
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Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) <ISSN:0001-3765>; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) <doi:10.1590/1678-992x-2016-0401>; and Vigour, in Medeiros and Pereira (2018) <doi:10.1590/1983-40632018v4852340>.
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2025-03-25 |
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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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2025-03-25 |
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r-secret
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Allow sharing sensitive information, for example passwords, 'API' keys, etc., in R packages, using public key cryptography.
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2025-03-25 |
