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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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2025-04-22 |
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r-semipar
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public |
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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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r-seer2r
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read and write SEER*STAT data files
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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
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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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2025-04-22 |
