All packages

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growthcurver — 0.3.1

Simple Metrics to Summarize Growth Curves

growthmodels — 1.3.1

Nonlinear Growth Models

growthPheno — 2.1.25

Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits

growthrate — 1.3

Bayesian reconstruction of growth velocity

growthrates — 0.8.4

Estimate Growth Rates from Experimental Data

grpCox — 1.0.2

Penalized Cox Model for High-Dimensional Data with Grouped Predictors

grPipe — 0.1.0

Graphviz Pipeline Plot Based on Grids (grPipe: Graphviz Pipeline)

grplasso — 0.4-7

Fitting User-Specified Models with Group Lasso Penalty

grpnet — 0.5

Group Elastic Net Regularized GLMs and GAMs

grpreg — 3.5.0

Regularization Paths for Regression Models with Grouped Covariates

grpsel — 1.3.1

Group Subset Selection

grpseq — 1.0

Group Sequential Analysis of Clinical Trials

grpSLOPE — 0.3.3

Group Sorted L1 Penalized Estimation

GrpString — 0.3.2

Patterns and Statistical Differences Between Two Groups of Strings

grr — 0.9.5

Alternative Implementations of Base R Functions

GRS.test — 1.2

GRS Test for Portfolio Efficiency, Its Statistical Power Analysis, and Optimal Significance Level Calculation

GRShiny — 1.0.0

Graded Response Model

GRSxE — 1.0.1

Testing Gene-Environment Interactions Through Genetic Risk Scores

grt — 0.2.1

General Recognition Theory

GRTo — 1.3

Tools for the Analysis of Gutenberg-Richter Distributions of Earthquake Magnitudes

grwat — 0.0.4

River Hydrograph Separation and Analysis

GSA — 1.03.3

Gene Set Analysis

GSA.UN — 1.0.0

Global Sensitivity Analysis Tool

GSAfisherCombined — 1.0

Gene Set Analysis with Fisher Combined Method

gsalib — 2.2.1

Utility Functions for 'GATK'

GSAQ — 1.0

Gene Set Analysis with QTL

gsarima — 0.1-5

Two Functions for Generalized SARIMA Time Series Simulation

gsbDesign — 1.0-3

Group Sequential Bayes Design

gsbm — 0.2.2

Estimate Parameters in the Generalized SBM

gscaLCA — 0.0.5

Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression

gscounts — 0.1-4

Group Sequential Designs with Negative Binomial Outcomes

gscramble — 1.0.1

Simulating Admixed Genotypes Without Replacement

GSD — 1.0.0

Graph Signal Decomposition

GSDA — 1.0

Gene Set Distance Analysis (GSDA)

gsDesign — 3.6.4

Group Sequential Design

gsDesign2 — 1.1.2

Group Sequential Design with Non-Constant Effect

GSE — 4.2-1

Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data

gsEasy — 1.5

Gene Set Enrichment Analysis in R

GseaVis — 0.0.5

Implement for 'GSEA' Enrichment Visualization

GSED — 2.6

Group Sequential Enrichment Design

gSeg — 1.0

Graph-Based Change-Point Detection (g-Segmentation)

GSelection — 0.1.0

Genomic Selection

gsheet — 0.4.5

Download Google Sheets Using Just the URL

gsignal — 0.3-7

Signal Processing

gsisdecoder — 0.0.1

High Efficient Functions to Decode NFL Player IDs

gsl — 2.1-8

Wrapper for the Gnu Scientific Library

gslnls — 1.3.2

GSL Multi-Start Nonlinear Least-Squares Fitting

gsloid — 0.2.0

Global Sea Level and Oxygen Isotope Data

GSM — 1.3.2

Gamma Shape Mixture

gsMAMS — 0.7.2

Group Sequential Designs of Multi-Arm Multi-Stage Trials

gsmoothr — 0.1.7

Smoothing tools

GSMX — 1.3

Multivariate Genomic Selection

GSODR — 4.1.2

Global Surface Summary of the Day ('GSOD') Weather Data Client

gson — 0.1.0

Base Class and Methods for 'gson' Format

GSparO — 1.0

Group Sparse Optimization

gspcr — 0.9.5

Generalized Supervised Principal Component Regression

gsrs — 0.1.1

A Group-Specific Recommendation System

gsrsb — 1.2.1

Group Sequential Refined Secondary Boundary

gss — 2.2-7

General Smoothing Splines

GSSE — 0.1

Genotype-Specific Survival Estimation

GSSTDA — 1.0.0

Progression Analysis of Disease with Survival using Topological Data Analysis

gstar — 0.1.0

Generalized Space-Time Autoregressive Model

gstat — 2.1-2

Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation

gStream — 0.2.0

Graph-Based Sequential Change-Point Detection for Streaming Data

gstsm — 1.0.0

Generalized Spatial-Time Sequence Miner

gsubfn — 0.7

Utilities for Strings and Function Arguments

gsw — 1.2-0

Gibbs Sea Water Functions

GsymPoint — 1.1.2

Estimation of the Generalized Symmetry Point, an Optimal Cutpoint in Continuous Diagnostic Tests

gsynth — 1.2.1

Generalized Synthetic Control Method

gt — 0.11.0

Easily Create Presentation-Ready Display Tables

gt4ireval — 2.0

Generalizability Theory for Information Retrieval Evaluation

gtable — 0.3.5

Arrange 'Grobs' in Tables

GTDL — 1.0.0

The Generalized Time-Dependent Logistic Family

gte — 1.2-3

Generalized Turnbull's Estimator

gTests — 0.2

Graph-Based Two-Sample Tests

gTestsMulti — 0.1.1

New Graph-Based Multi-Sample Tests

gtexr — 0.1.0

Query the GTEx Portal API

gtExtras — 0.5.0

Extending 'gt' for Beautiful HTML Tables

gtexture — 1.0.0

Generalized Application of Co-Occurrence Matrices and Haralick Texture

gtfsio — 1.1.1

Read and Write General Transit Feed Specification (GTFS) Files

gtfsrouter — 0.1.2

Routing with 'GTFS' (General Transit Feed Specification) Data

gtheory — 0.1.2

Apply Generalizability Theory with R

gto — 0.1.1

Insert 'gt' Tables into Word Documents

gtools — 3.9.5

Various R Programming Tools

gtranslate — 0.0.1

Translate Between Different Languages

gtreg — 0.4.0

Regulatory Tables for Clinical Research

gtrendsR — 1.5.1

Perform and Display Google Trends Queries

gtsummary — 2.0.2

Presentation-Ready Data Summary and Analytic Result Tables

gtWAS — 1.1.0

Genome and Transcriptome Wide Association Study

guaguas — 0.3.0

Nombres Inscritos en Chile (1920 - 2021)

guardianapi — 0.1.1

Access 'The Guardian' Newspaper Open Data API

GUD — 1.0.2

Bayesian Modal Regression Based on the GUD Family

Guerry — 1.8.3

Maps, Data and Methods Related to Guerry (1833) "Moral Statistics of France"

guescini — 0.1.0

Real-Time PCR Data Sets by Guescini et al. (2008)

guess — 0.1

Adjust Estimates of Learning for Guessing

GUEST — 0.2.0

Graphical Models in Ultrahigh-Dimensional and Error-Prone Data via Boosting Algorithm

GUIDE — 1.2.7

GUI for DErivatives in R

guidedPLS — 1.0.0

Supervised Dimensional Reduction by Guided Partial Least Squares

guildai — 0.0.1

Track Machine Learning Experiments

GUILDS — 1.4.6

Implementation of Sampling Formulas for the Unified Neutral Model of Biodiversity and Biogeography, with or without Guild Structure

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