|
r-suntersampling
|
public |
Functions for drawing samples according to Sunter's sampling design, and for computing first and second order inclusion probabilities
|
2025-04-22 |
|
r-sunclarco
|
public |
Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) <DOI:10.1111/rssb.12174>).
|
2025-04-22 |
|
r-suncalc
|
public |
Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.), moon position and lunar phase for the given location and time. Most calculations are based on the formulas given in Astronomy Answers articles about position of the sun and the planets : <https://www.aa.quae.nl/en/reken/zonpositie.html>.
|
2025-04-22 |
|
r-sue
|
public |
This is a package for the subsampling method of robust estimation of linear regression models
|
2025-04-22 |
|
r-sudokualt
|
public |
Tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, 'sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.
|
2025-04-22 |
|
r-sudoku
|
public |
Generates, plays, and solves Sudoku puzzles. The GUI playSudoku() needs package "tkrplot" if you are not on Windows.
|
2025-04-22 |
|
r-subsamp
|
public |
This subsample winner algorithm (SWA) for regression with a large-p data (X, Y) selects the important variables (or features) among the p features X in explaining the response Y. The SWA first uses a base procedure, here a linear regression, on each of subsamples randomly drawn from the p variables, and then computes the scores of all features, i.e., the p variables, according to the performance of these features collected in each of the subsample analyses. It then obtains the 'semifinalist' of the features based on the resulting scores and determines the 'finalists', i.e., the important features, from the 'semifinalist'. Fan, Sun and Qiao (2017) <http://sr2c.case.edu/swa-reg/>.
|
2025-04-22 |
|
r-subpathwaygmir
|
public |
Routines for identifying metabolic subpathways mediated by microRNAs (miRNAs) through topologically locating miRNAs and genes within reconstructed Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway graphs embedded by miRNAs. (1) This package can obtain the reconstructed KEGG metabolic pathway graphs with genes and miRNAs as nodes, through converting KEGG metabolic pathways to graphs with genes as nodes and compounds as edges, and then integrating miRNA-target interactions verified by low-throughput experiments from four databases (TarBase, miRecords, mirTarBase and miR2Disease) into converted pathway graphs. (2) This package can locate metabolic subpathways mediated by miRNAs by topologically analyzing the "lenient distance" of miRNAs and genes within reconstructed KEGG metabolic pathway graphs.(3) This package can identify significantly enriched miRNA-mediated metabolic subpathways based on located subpathways by hypergenomic test. (4) This package can support six species for metabolic subpathway identification, such as caenorhabditis elegans, drosophila melanogaster, danio rerio, homo sapiens, mus musculus and rattus norvegicus, and user only need to update interested organism-specific environment variables.
|
2025-04-22 |
|
r-subniche
|
public |
Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays.
|
2025-04-22 |
|
r-subgxe
|
public |
Classical methods for combining summary data from genome-wide association studies (GWAS) only use marginal genetic effects and power can be compromised in the presence of heterogeneity. 'subgxe' is a R package that implements p-value assisted subset testing for association (pASTA), a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA generalizes association analysis based on subsets by incorporating gene-environment interactions into the testing procedure.
|
2025-04-22 |
|
r-subgroup.discovery
|
public |
Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of "rules" which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest. Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.
|
2025-04-22 |
|
r-subgroup
|
public |
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
|
2025-04-22 |
|
r-subdetect
|
public |
A test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.
|
2025-04-22 |
|
r-subcultcon
|
public |
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.
|
2025-04-22 |
|
r-subcopem2d
|
public |
Calculate empirical subcopula and dependence measures from a given bivariate sample, and Bernstein copula approximations.
|
2025-04-22 |
|
r-stylest
|
public |
Estimates distinctiveness in speakers' (authors') style. Fits models that can be used for predicting speakers of new texts. Methods developed in Spirling et al (2018) <doi:10.2139/ssrn.3235506> (working paper).
|
2025-04-22 |
|
r-stv
|
public |
Implementations of the Single Transferable Vote counting system. By default, it uses the Cambridge method for surplus allocation and Droop method for quota calculation. Fractional surplus allocation and the Hare quota are available as options.
|
2025-04-22 |
|
r-stubthat
|
public |
Create stubs of functions for use while testing.
|
2025-04-22 |
|
r-stuart
|
public |
Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.
|
2025-04-22 |
|
r-stsm.class
|
public |
This package defines an S4 class for structural time series models and provides some basic methods to work with it.
|
2025-04-22 |
|
r-structfdr
|
public |
Perform more powerful false discovery control (FDR) for microbiome data, taking into account the prior phylogenetic relationship among bacteria species. As a general methodology, it is applicable to any type of (genomic) data with prior structure information.
|
2025-04-22 |
|
r-stripless
|
public |
For making Trellis-type conditioning plots without strip labels. This is useful for displaying the structure of results from factorial designs and other studies when many conditioning variables would clutter the display with layers of redundant strip labels. Settings of the variables are encoded by layout and spacing in the trellis array and decoded by a separate legend. The functionality is implemented by a single S3 generic strucplot() function that is a wrapper for the Lattice package's xyplot() function. This allows access to all Lattice graphics capabilities in the usual way.
|
2025-04-22 |
|
r-strip
|
public |
The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.
|
2025-04-22 |
|
r-stringformattr
|
public |
Pass named and unnamed character vectors into specified positions in strings. This represents an attempt to replicate some of python's string formatting.
|
2025-04-22 |
|
r-stringb
|
public |
Base R already ships with string handling capabilities 'out- of-the-box' but lacks streamlined function names and workflow. The 'stringi' ('stringr') package on the other hand has well named functions, extensive Unicode support and allows for a streamlined workflow. On the other hand it adds dependencies and regular expression interpretation between base R functions and 'stringi' functions might differ. This packages aims at providing a solution to the use case of unwanted dependencies on the one hand but the need for streamlined text processing on the other. The packages' functions are solely based on wrapping base R functions into 'stringr'/'stringi' like function names. Along the way it adds one or two extra functions and last but not least provides all functions as generics, therefore allowing for adding methods for other text structures besides plain character vectors.
|
2025-04-22 |
