R-universe search: exports:sample

Showing 12 of total 12 results (show query)

tidymodels

dials:Tools for Creating Tuning Parameter Values

Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.

Maintained by Hannah Frick. Last updated 1 months ago.

114 stars 14.31 score 426 scripts 52 dependents

dicook

nullabor:Tools for Graphical Inference

Tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Maintained by Di Cook. Last updated 2 months ago.

57 stars 10.38 score 370 scripts 2 dependents

bioc

RnaSeqSampleSize:RnaSeqSampleSize

RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

Maintained by Shilin Zhao Developer. Last updated 5 months ago.

immunooncology experimentaldesign sequencing rnaseq geneexpression differentialexpression cpp

5.30 score 20 scripts

poissonconsulting

embr:Model Builder Utility Functions and Virtual Classes

Utility functions and virtual classes shared by model builder packages such as tmbr, jmbr and smbr.

Maintained by Joe Thorley. Last updated 2 months ago.

analyses mbr

3 stars 4.61 score 4 scripts 3 dependents

radiant-rstats

radiant.design:Design Menu for Radiant: Business Analytics using R and Shiny

The Radiant Design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in 'radiant.data'.

Maintained by Vincent Nijs. Last updated 11 months ago.

11 stars 4.57 score 19 scripts 1 dependents

dominiquemaucieri

quadcleanR:Cleanup and Visualization of Quadrat Data

A tool that can be customized to aid in the clean up of ecological data collected using quadrats and can crop quadrats to ensure comparability between quadrats collected under different methodologies.

Maintained by Dominique Maucieri. Last updated 2 years ago.

4.45 score 14 scripts

sidiwang

snSMART:Small N Sequential Multiple Assignment Randomized Trial Methods

Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.

Maintained by Michael Kleinsasser. Last updated 6 months ago.

bayesian-analysis clinical-trials rare-disease small-sample jags cpp

1 stars 3.90 score 3 scripts

kaneplusplus

preference:2-Stage Preference Trial Design and Analysis

Design and analyze two-stage randomized trials with a continuous outcome measure. The package contains functions to compute the required sample size needed to detect a given preference, treatment, and selection effect; alternatively, the package contains functions that can report the study power given a fixed sample size. Finally, analysis functions are provided to test each effect using either summary data (i.e. means, variances) or raw study data. <doi:10.18637/jss.v094.c02> <doi:10.1002/sim.7830>

Maintained by Michael Kane. Last updated 5 years ago.

clinical-trial-design clinical-trials preference-design

3 stars 3.22 score 11 scripts

eliasmacielr

msu:Multivariate Symmetric Uncertainty and Other Measurements

Estimators for multivariate symmetrical uncertainty based on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>, total correlation, information gain and symmetrical uncertainty of categorical variables.

Maintained by Elias Maciel. Last updated 7 years ago.

1 stars 2.74 score 11 scripts

kathbaum

DrDimont:Drug Response Prediction from Differential Multi-Omics Networks

While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only to the software and explicitly not to the included data.

Maintained by Katharina Baum. Last updated 3 years ago.

2.00 score 2 scripts

sth1402

subdetect:Detect Subgroup with an Enhanced Treatment Effect

A test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.

Maintained by Shannon T. Holloway. Last updated 3 years ago.

1.00 score 2 scripts

cran

frequentistSSD:Screened Selection Design with Survival Endpoints

A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the median survival time) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. Though The survival endpoint is often encountered in phase II trials, the existing SSD methods cannot deal with the survival endpoint. Furthermore, the existing SSD won’t control the type I error rate. The proposed designs can “partially” control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.

Maintained by Chia-Wei Hsu. Last updated 3 years ago.

1.00 score