All packages

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stringx — 0.2.9

Replacements for Base String Functions Powered by 'stringi'

strip — 1.0.0

Lighten your R Model Outputs

stripless — 1.0-3

Structured Trellis Displays Without Strips for Lattice Graphics

striprtf — 0.6.0

Extract Text from RTF File

stRoke — 24.10.1

Clinical Stroke Research

StroupGLMM — 0.3.0

R Codes and Datasets for Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup

strs — 0.1.0

'Python' Style String Functions

strucchange — 1.5-4

Testing, Monitoring, and Dating Structural Changes

strucchangeRcpp — 1.5-4-1.0.0

Testing, Monitoring, and Dating Structural Changes: C++ Version

structree — 1.1.7

Tree-Structured Clustering

StructuralDecompose — 0.1.1

Decomposes a Level Shifted Time Series

StructureMC — 1.0

Structured Matrix Completion

strvalidator — 2.4.1

Process Control and Validation of Forensic STR Kits

sts — 1.4

Estimation of the Structural Topic and Sentiment-Discourse Model for Text Analysis

sTSD — 0.2.0

Simulate Time Series Diagnostics

stuart — 0.10.2

Subtests Using Algorithmic Rummaging Techniques

studentlife — 1.1.0

Tidy Handling and Navigation of the Student-Life Dataset

studyStrap — 1.0.0

Study Strap and Multi-Study Learning Algorithms

StuteTest — 1.0.2

Stute (1997) Linearity Test

STV — 1.0.2

Single Transferable Vote Counting

stxplore — 0.1.0

Exploration of Spatio-Temporal Data

styler — 1.10.3

Non-Invasive Pretty Printing of R Code

stylest2 — 0.1

Estimating Speakers of Texts

stylo — 0.7.5

Stylometric Multivariate Analyses

subcopem2D — 1.3

Bivariate Empirical Subcopula

subdetect — 1.3

Detect Subgroup with an Enhanced Treatment Effect

subformula — 0.1.0

Create Subformulas of a Formula

subgroup — 1.1

Methods for exploring treatment effect heterogeneity in subgroup analysis of clinical trials

SubgrpID — 0.12

Patient Subgroup Identification for Clinical Drug Development

subgxe — 0.9.0

Combine Multiple GWAS by Using Gene-Environment Interactions

subincomeR — 0.3.0

Access to Global Sub-National Income Data

submax — 1.1.5

Effect Modification in Observational Studies Using the Submax Method

SubpathwayLNCE — 1.0

Identify Signal Subpathways Competitively Regulated by LncRNAs Based on ceRNA Theory

subplex — 1.9

Unconstrained Optimization using the Subplex Algorithm

subrank — 0.9.9.3

Computes Copula using Ranks and Subsampling

subsampling — 0.1.1

Optimal Subsampling Methods for Statistical Models

subscore — 3.3

Computing Subscores in Classical Test Theory and Item Response Theory

subscreen — 4.0.1

Systematic Screening of Study Data for Subgroup Effects

subselect — 0.16.0

Selecting Variable Subsets

subsemble — 0.1.0

An Ensemble Method for Combining Subset-Specific Algorithm Fits

subspace — 1.0.4

Interface to OpenSubspace

SubTite — 4.0.5

Subgroup Specific Optimal Dose Assignment

SubTS — 1.0

Positive Tempered Stable Distributions and Related Subordinators

SubtypeDrug — 0.1.9

Prioritization of Candidate Cancer Subtype Specific Drugs

SubVis — 2.0.2

Visual Exploration of Protein Alignments Resulting from Multiple Substitution Matrices

success — 1.1.0

Survival Control Charts Estimation Software

sudachir — 0.1.0

R Interface to 'Sudachi'

suddengains — 0.7.2

Identify Sudden Gains in Longitudinal Data

sudoku — 2.8

Sudoku Puzzle Generator and Solver

sudokuAlt — 0.2-1

Tools for Making and Spoiling Sudoku Games

SudokuDesigns — 1.2.0

Sudoku as an Experimental Design

SuessR — 0.1.6

Suess and Laws Corrections for Marine Stable Carbon Isotope Data

sufficientForecasting — 0.1.0

Sufficient Forecasting using Factor Models

sugarbag — 0.1.6

Create Tessellated Hexagon Maps

sugarglider — 1.0.3

Create Glyph-Maps of Spatiotemporal Data

suggests — 0.1.0

Declare when Suggested Packages are Needed

sugrrants — 0.2.9

Supporting Graphs for Analysing Time Series

SumcaVer1 — 0.1.0

Mean Square Prediction Error Estimation in Small Area Estimation

sumFREGAT — 1.2.5

Fast Region-Based Association Tests on Summary Statistics

summariser — 2.3.0

Easy Calculation and Visualisation of Confidence Intervals

SummaryLasso — 1.2.1

Building Polygenic Risk Score Using GWAS Summary Statistics

summarytools — 1.1.4

Tools to Quickly and Neatly Summarize Data

summclust — 0.7.2

Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors

SUMMER — 2.0.0

Small-Area-Estimation Unit/Area Models and Methods for Estimation in R

SUMO — 0.2.0

Generating Multi-Omics Datasets for Testing and Benchmarking

sumR — 0.4.15

Approximate Summation of Series

sumSome — 1.1.0

Permutation True Discovery Guarantee by Sum-Based Tests

sunburstR — 2.1.8

Sunburst 'Htmlwidget'

suncalc — 0.5.1

Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase

SunCalcMeeus — 0.1.2

Sun Position and Daylight Calculations

Sunclarco — 1.0.0

Survival Analysis using Copulas

sundialr — 0.1.6.2

An Interface to 'SUNDIALS' Ordinary Differential Equation (ODE) Solvers

SUNGEO — 1.3.0

Sub-National Geospatial Data Archive: Geoprocessing Toolkit

SunsVoc — 0.1.2

Constructing Suns-Voc from Outdoor Time-Series I-V Curves

suntools — 1.0.1

Calculate Sun Position, Sunrise, Sunset, Solar Noon and Twilight

supclust — 1.1-1

Supervised Clustering of Predictor Variables Such as Genes

super — 0.1.1

Interpreted String Literals

superb — 0.95.19

Summary Plots with Adjusted Error Bars

superbiclust — 1.2

Generating Robust Biclusters from a Bicluster Set (Ensemble Biclustering)

SuperCell — 1.0.1

Simplification of scRNA-Seq Data by Merging Together Similar Cells

supercells — 1.0.0

Superpixels of Spatial Data

supercompress — 1.1

Supervised Compression of Big Data

superdiag — 2.0

A Comprehensive Test Suite for Testing Markov Chain Nonconvergence

SuperExactTest — 1.1.0

Exact Test and Visualization of Multi-Set Intersections

SuperGauss — 2.0.3

Superfast Likelihood Inference for Stationary Gaussian Time Series

superheat — 0.1.0

A Graphical Tool for Exploring Complex Datasets Using Heatmaps

SuperLearner — 2.0-29

Super Learner Prediction

superMICE — 1.1.1

SuperLearner Method for MICE

superml — 0.5.7

Build Machine Learning Models Like Using Python's Scikit-Learn Library in R

supernova — 3.0.0

Judd, McClelland, & Ryan Formatting for ANOVA Output

superpc — 1.12

Supervised Principal Components

SuperpixelImageSegmentation — 1.0.5

Superpixel Image Segmentation

Superpower — 0.2.0

Simulation-Based Power Analysis for Factorial Designs

SuperRanker — 1.2.1

Sequential Rank Agreement

superspreading — 0.3.0

Understand Individual-Level Variation in Infectious Disease Transmission

supervisedPRIM — 2.0.0

Supervised Classification Learning and Prediction using Patient Rule Induction Method (PRIM)

SupMZ — 0.2.0

Detecting Structural Change with Heteroskedasticity

SuppDists — 1.1-9.9

Supplementary Distributions

support.BWS — 0.4-6

Tools for Case 1 Best-Worst Scaling

support.BWS2 — 0.4-0

Tools for Case 2 Best-Worst Scaling

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