All packages

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S7 — 0.2.0

An Object Oriented System Meant to Become a Successor to S3 and S4

saasCNV — 0.3.4

Somatic Copy Number Alteration Analysis Using Sequencing and SNP Array Data

sabre — 0.4.3

Spatial Association Between Regionalizations

sac — 1.0.2

Semiparametric Analysis of Change-Point

saccadr — 0.1.3

Extract Saccades via an Ensemble of Methods Approach

sad — 0.1.3

Verify the Scale, Anisotropy and Direction of Weather Forecasts

SADEG — 1.0.0

Stability Analysis in Differentially Expressed Genes

SADISA — 1.2

Species Abundance Distributions with Independent-Species Assumption

sadists — 0.2.5

Some Additional Distributions

sads — 0.6.3

Maximum Likelihood Models for Species Abundance Distributions

sae — 1.3

Small Area Estimation

sae.prop — 0.1.2

Small Area Estimation using Fay-Herriot Models with Additive Logistic Transformation

sae2 — 1.2-1

Small Area Estimation: Time-Series Models

saeBest — 0.1.0

Selecting Auxiliary Variables in Small Area Estimation (SAE) Model

saebnocov — 0.1.0

Small Area Estimation using Empirical Bayes without Auxiliary Variable

saeczi — 0.2.0

Small Area Estimation for Continuous Zero Inflated Data

saeeb — 0.1.0

Small Area Estimation for Count Data

saeHB — 0.2.2

Small Area Estimation using Hierarchical Bayesian Method

saeHB.ME — 1.0.1

Small Area Estimation with Measurement Error using Hierarchical Bayesian Method

saeHB.ME.beta — 1.1.0

SAE with Measurement Error using HB under Beta Distribution

saeHB.panel — 0.1.1

Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model

saeHB.panel.beta — 0.1.5

Small Area Estimation using HB for Rao Yu Model under Beta Distribution

saeHB.spatial — 0.1.1

Small Area Estimation Hierarchical Bayes For Spatial Model

saeHB.unit — 0.1.0

Basic Unit Level Model using Hierarchical Bayesian Approach

saeHB.ZIB — 0.1.1

Small Area Estimation using Hierarchical Bayesian under Zero Inflated Binomial Distribution

saekernel — 0.1.1

Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

saeME — 1.3.1

Small Area Estimation with Measurement Error

saemix — 3.3

Stochastic Approximation Expectation Maximization (SAEM) Algorithm

saeMSPE — 1.3

Computing MSPE Estimates in Small Area Estimation

saens — 0.1.2

Small Area Estimation with Cluster Information for Estimation of Non-Sampled Areas

saePseudo — 0.1.0

Small Area Estimation using Averaging Pseudo Area Level Model

saeRobust — 0.5.0

Robust Small Area Estimation

saeSim — 0.11.0

Simulation Tools for Small Area Estimation

saeTrafo — 1.0.4

Transformations for Unit-Level Small Area Models

SAEval — 1.0.0

Small Area Estimation Evaluation

SAFARI — 0.1.0

Shape Analysis for AI-Reconstructed Images

SAFD — 2.1

Statistical Analysis of Fuzzy Data

safejoin — 0.2.0

Perform "Safe" Table Joins

safer — 0.2.1

Encrypt and Decrypt Strings, R Objects and Files

safestats — 0.8.7

Safe Anytime-Valid Inference

safetensors — 0.1.2

Safetensors File Format

safetyCharts — 0.3.0

Charts for Monitoring Clinical Trial Safety

safetyData — 1.0.0

Clinical Trial Data

safetyGraphics — 2.1.1

Interactive Graphics for Monitoring Clinical Trial Safety

SafeVote — 1.0.1

Election Vote Counting with Safety Features

sageR — 0.6.1

Applied Statistics for Economics and Management with R

SAGM — 1.0.0

Spatial Autoregressive Graphical Model

SAGMM — 0.2.4

Clustering via Stochastic Approximation and Gaussian Mixture Models

sahpm — 1.0.1

Variable Selection using Simulated Annealing

sAIC — 1.0.1

Akaike Information Criterion for Sparse Estimation

SailoR — 1.2

An Extension of the Taylor Diagram to Two-Dimensional Vector Data

SAiVE — 1.0.6

Functions Used for SAiVE Group Research, Collaborations, and Publications

salad — 1.1

Simple Automatic Differentiation

SALES — 1.0.2

The (Adaptive) Elastic Net and Lasso Penalized Sparse Asymmetric Least Squares (SALES) and Coupled Sparse Asymmetric Least Squares (COSALES) using Coordinate Descent and Proximal Gradient Algorithms

salesforcer — 1.0.2

An Implementation of 'Salesforce' APIs Using Tidy Principles

salso — 0.3.42

Search Algorithms and Loss Functions for Bayesian Clustering

SALTSampler — 1.1.0

Efficient Sampling on the Simplex

salty — 0.1.1

Turn Clean Data into Messy Data

SAM — 1.1.3

Sparse Additive Modelling

samadb — 0.3.0

South Africa Macroeconomic Database API

SAMBA — 0.9.0

Selection and Misclassification Bias Adjustment for Logistic Regression Models

sambia — 0.1.0

A Collection of Techniques Correcting for Sample Selection Bias

samc — 4.0.0

Spatial Absorbing Markov Chains

SAME — 0.1.0

Seamless Adaptive Multi-Arm Multi-Stage Enrichment

SAMGEP — 0.1.0-1

A Semi-Supervised Method for Prediction of Phenotype Event Times

samon — 4.0.2

Sensitivity Analysis for Missing Data

sampbias — 2.0.0

Evaluating Geographic Sampling Bias in Biological Collections

sampcompR — 0.2.4

Comparing and Visualizing Differences Between Surveys

sampledatasets — 0.1.0

A Collection of Sample Datasets

sampler — 0.2.4

Sample Design, Drawing & Data Analysis Using Data Frames

sampleSelection — 1.2-12

Sample Selection Models

samplesize — 0.2-4

Sample Size Calculation for Various t-Tests and Wilcoxon-Test

SampleSize4ClinicalTrials — 0.2.3

Sample Size Calculation for the Comparison of Means or Proportions in Phase III Clinical Trials

samplesize4surveys — 4.1.1

Sample Size Calculations for Complex Surveys

samplesizeCMH — 0.0.3

Power and Sample Size Calculation for the Cochran-Mantel-Haenszel Test

SampleSizeDiagnostics — 0.1.0

Choosing Sample Size for Evaluating a Diagnostic Test

samplesizeestimator — 1.0.0

Calculate Sample Size for Various Scenarios

samplesizelogisticcasecontrol — 2.0.2

Sample Size and Power Calculations for Case-Control Studies

SampleSizeMeans — 1.2.3

Sample Size Calculations for Normal Means

SampleSizeProportions — 1.1.3

Calculating Sample Size Requirements when Estimating the Difference Between Two Binomial Proportions

sampleVADIR — 1.0.0

Draw Stratified Samples from the VADIR Database

sampling — 2.10

Survey Sampling

SamplingBigData — 1.0.0

Sampling Methods for Big Data

samplingbook — 1.2.4

Survey Sampling Procedures

samplingDataCRT — 1.0

Sampling Data Within Different Study Designs for Cluster Randomized Trials

samplingin — 1.1.1

Dynamic Survey Sampling Solutions

samplingR — 1.0.1

Sampling and Estimation Methods

SamplingStrata — 1.5-4

Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys

samplingVarEst — 1.5

Sampling Variance Estimation

samplr — 1.0.1

Compare Human Performance to Sampling Algorithms

samplrData — 1.0.0

Datasets from the SAMPLING Project

SAMprior — 1.1.1

Self-Adapting Mixture (SAM) Priors

sampsizeval — 1.0.0.0

Sample Size for Validation of Risk Models with Binary Outcomes

samr — 3.0

SAM: Significance Analysis of Microarrays

sams — 0.4.3

Merge-Split Samplers for Conjugate Bayesian Nonparametric Models

SAMtool — 1.8.0

Stock Assessment Methods Toolkit

SAMTx — 0.3.0

Sensitivity Assessment to Unmeasured Confounding with Multiple Treatments

SAMUR — 1.1

Stochastic Augmentation of Matched Data Using Restriction Methods

samurais — 0.1.0

Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

sand — 2.0.0

Statistical Analysis of Network Data with R, 2nd Edition

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