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adoptr

Adaptive optimal two-stage designs for clinical trials with one or two arms.

Installation

install the development version directly from GitHub with:

# install.packages("devtools")
devtools::install_github("kkmann/adoptr")

Documentation

The documentation is hosted at https://kkmann.github.io/adoptr.

Validation

We provide an extensive validation suite for adoptr in the separate package adoptrValidation. The rationale behind externalizing the validation suit is to keep the main package test suit lean and focused on checking technical correctness. We also want to ensure that the validation suit is transparent and accessible. It is thus implemented as a set of vignettes in the separate package adoptrValidation. The entire validation report is made accessible at https://kkmann.github.io/adoptrValidation/. The website is re-build on a weekly basis to ensure that the presented validation report is up-to-date with the master branch of adoptr. To validate a specific version of adoptr, just download and install the validation package before building the contained vignettes locally.

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Version

Install

install.packages('adoptr')

Monthly Downloads

416

Version

0.1.1

License

MIT + file LICENSE

Issues

Pull Requests

Stars

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Maintainer

Kevin Kunzmann

Last Published

April 1st, 2019

Functions in adoptr (0.1.1)

GroupSequentialDesign-class

Group-sequential two-stage designs
IntegralScore-class

N1-class

Regularize n1
bounds

Get support of a prior or data distribution
condition

Condition a prior on an interval
evaluate

Evaluation of a score
expectation

Expected value of a function
plot,TwoStageDesign-method

Plot TwoStageDesign with optional set of conditional scores
posterior

Compute posterior distribution
ConstraintsCollection-class

Collection of constraints
simulate,TwoStageDesign,numeric-method

Draw samples from a two-stage design
subject_to

Create a collection of constraints
AffineScore-class

Affine functions of scores
AverageN2-class

Regularization via L1 norm
ContinuousPrior-class

Continuous univariate prior distributions
minimize

Find optimal two-stage design by constraint minimization
ConditionalPower-class

Conditional power of a design given stage-one outcome
ConditionalSampleSize-class

Conditional sample size of a design given stage-one outcome
PointMassPrior-class

Univariate discrete point mass priors
Prior-class

Univariate prior on model parameter
Normal-class

Normal data distribution
n1

Query sample size of a design
c2

Query critical values of a design
predictive_cdf

Predictive CDF
predictive_pdf

Predictive PDF
ConditionalScore-class

Class for conditional scoring function
cumulative_distribution_function

Cumulative distribution function
Constraint-class

Formulating constraints
probability_density_function

Probability density function
OneStageDesign-class

One-stage designs
adoptr

Adaptive Optimal Two-Stage Designs
tunable_parameters

Switch between numeric and S4 class representation of a design
score-arithmetic

Score arithmetic
get_lower_boundary_design

Boundary designs
expected

Compute the expectation of a conditional score
make_tunable

Fix parameters during optimization
TwoStageDesign-class

Two-stage designs
DataDistribution-class

Data distributions
UnconditionalScore-class

Class for unconditional scoring function