skpr

Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

https://github.com/tylermorganwall/skpr

Science Score: 26.0%

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Low similarity (18.9%) to scientific vocabulary

Keywords

design-of-experiments linear-models linear-regression monte-carlo optimal-designs power r rstats split-plot-designs survival-analysis

Last synced: 6 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: tylermorganwall
License: gpl-3.0
Language: R
Default Branch: master
Homepage: https://tylermorganwall.github.io/skpr/
Size: 40.4 MB

Statistics

Stars: 121
Watchers: 11
Forks: 15
Open Issues: 4
Releases: 1

Topics

design-of-experiments linear-models linear-regression monte-carlo optimal-designs power r rstats split-plot-designs survival-analysis

Created over 9 years ago · Last pushed 11 months ago

Metadata Files

Readme Changelog License

README.Rmd

---
output: 
  github_document:
  html_preview: false
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# skpr 



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## Overview

**skpr** is an open source design of experiments suite for generating and evaluating optimal designs in R. Here is a sampling of what skpr offers: 

* Generates and evaluates D, I, A, Alias, E, T, and G optimal designs, as well as user-defined custom optimality criteria. 
* Supports generation and evaluation of split/split-split/.../N-split plot designs. 
* Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. 
* Provides an extensible framework for the user to evaluate Monte Carlo power using their own libraries. 
* Includes a Shiny graphical user interface, skprGUI, that auto-generates the R code used to create and evaluate the design to improve ease-of-use and enhance reproducibility.

## Installation

```{r, eval=FALSE}
# To install:
install.packages("skpr")

# To install the latest version from Github:
# install.packages("devtools")
devtools::install_github("tylermorganwall/skpr")
```

## Functions

* `gen_design()` generates optimal designs from a candidate set, given a model and the desired number of runs.
* `eval_design()` evaluates power parametrically for linear models, for normal and split-plot designs.
* `eval_design_mc()` evaluates power with a Monte Carlo simulation, for linear and generalized linear models. This function also supports calculating power for split-plot designs using REML. 
* `eval_design_survival_mc()` evaluates power with a Monte Carlo simulation, allowing the user to specify a point at which the data is censored.
* `eval_design_custom_mc()` allows the user to import their own libraries and use the Monte Carlo framework provided by skpr to calculate power.
* `calculate_power_curves()` provides an interface to automate the generation and evaluation of designs to create power versus sample size and effect size curves.
* `skprGUI()` opens up the GUI in either RStudio or an external browser.

If addition, the package offers two functions to generate common plots related to designs:

* `plot_correlations()` generates a color map of correlations between variables.
* `plot_fds()` generates the fraction of design space plot for a given design.

##skprGUI
 
`skprGUI()` provides an graphical user interface to access all of the main features of skpr. An interactive tutorial is provided to familiarize the user with the available functionality. Type `skprGUI()` to begin. Screenshots:




## Usage

```{r, include=FALSE}
set.seed(2)
```

```{r example, message = FALSE}
library(skpr)

#Generate a candidate set of all potential design points to be considered in the experiment
#The hypothetical experiment is determining what affects the caffeine content in coffee
candidate_set = expand.grid(temp = c(80,90,100), 
                            type = c("Kona","Java"),
                            beansize = c("Large","Medium","Small"))
candidate_set

#Generate the design (default D-optimal)
design = gen_design(candidateset = candidate_set, 
                    model = ~temp + type + beansize,
                    trials=12)
design

#Evaluate power for the design with an allowable type-I error of 5% (default)
eval_design(design)

#Evaluate power for the design using a Monte Carlo simulation. 
#Here, we set the effect size (here, the signal-to-noise ratio) to 1.5.
eval_design_mc(design, effectsize=1.5)

#Evaluate power for the design using a Monte Carlo simulation, for a non-normal response. 
#Here, we also increase the number of simululations to improve the precision of the results.
eval_design_mc(design, nsim=5000, glmfamily = "poisson", effectsize=c(2,6))

#skpr was designed to operate with the pipe (|>) in mind. 
#Here is an example of an entire design of experiments analysis in three lines:

expand.grid(temp = c(80,90,100), type = c("Kona","Java"), beansize = c("Large","Medium","Small")) |>
  gen_design(model = ~temp + type + beansize + beansize:type + I(temp^2), trials=24, optimality="I") |>
  eval_design_mc(detailedoutput = TRUE)

```

Owner

Login: tylermorganwall
Kind: user

Repositories: 31
Profile: https://github.com/tylermorganwall

GitHub Events

Total

Issues event: 1
Watch event: 8
Push event: 20
Create event: 1

Last Year

Issues event: 1
Watch event: 8
Push event: 20
Create event: 1

Committers

Last synced: 9 months ago

All Time

Total Commits: 545
Total Committers: 6
Avg Commits per committer: 90.833
Development Distribution Score (DDS): 0.09

Past Year

Commits: 14
Committers: 1
Avg Commits per committer: 14.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
tylermorganwall	t**w@g**m	496
George Khoury	g**y@g**m	33
Khoury	g**y@i**g	12
Thomas Holder	t**r@l**m	2
bpeaden	4****n	1
Dirk Eddelbuettel	e**d@d**g	1

Committer Domains (Top 20 + Academic)

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Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 38
Total pull requests: 45
Average time to close issues: 4 months
Average time to close pull requests: 5 days
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Total pull request authors: 5
Average comments per issue: 3.53
Average comments per pull request: 0.51
Merged pull requests: 41
Bot issues: 0
Bot pull requests: 0

Past Year

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Pull requests: 0
Average time to close issues: N/A
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Pull request authors: 0
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Top Authors

Issue Authors

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comatrion (5)
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tylermorganwall (12)
eddelbuettel (1)
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Top Labels

Issue Labels

enhancement (1)

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Packages

Total packages: 2
Total downloads:
- cran 1,422 last-month
Total docker downloads: 21,777

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 0
(may contain duplicates)
Total versions: 30
Total maintainers: 1

cran.r-project.org: skpr

Design of Experiments Suite: Generate and Evaluate Optimal Designs

Homepage: https://github.com/tylermorganwall/skpr
Documentation: http://cran.r-project.org/web/packages/skpr/skpr.pdf
License: GPL-3
Latest release: 1.8.2
published 10 months ago

Versions: 25
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 1,422 Last month
Docker Downloads: 21,777

Rankings

Stargazers count: 4.3%

Forks count: 5.0%

Downloads: 11.7%

Average: 17.3%

Dependent packages count: 29.8%

Dependent repos count: 35.5%

Maintainers (1)

tylermw@gmail.com

Last synced: 6 months ago

conda-forge.org: r-skpr

Homepage: https://github.com/tylermorganwall/skpr
License: GPL-3.0-only
Latest release: 1.1.6
published over 3 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 31.9%

Dependent repos count: 34.0%

Forks count: 36.7%

Average: 38.5%

Dependent packages count: 51.2%

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

R >= 3.0.2 depends
shiny * depends
Rcpp >= 0.11.0 imports
car * imports
doParallel * imports
doRNG * imports
foreach * imports
future * imports
gt * imports
iterators * imports
lazyeval * imports
lme4 * imports
lmerTest * imports
magrittr * imports
methods * imports
progress * imports
promises * imports
rintrojs * imports
scales * imports
shinyjs * imports
shinythemes * imports
stats * imports
survival * imports
utils * imports
viridis * imports
mbest * suggests
testthat * suggests

.github/workflows/R-CMD-check.yaml actions

actions/checkout v2 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite