bigmemory - Manage Massive Matrices with Shared Memory and Memory-Mapped Files

Create, store, access, and manipulate massive matrices. Matrices are allocated to shared memory and may use memory-mapped files. Packages 'biganalytics', 'bigtabulate', 'synchronicity', and 'bigalgebra' provide advanced functionality.

Last updated 8 months ago

126 stars 6.50 score 4 dependencies 59 dependents

listdown - Create R Markdown from Lists

Programmatically create R Markdown documents from lists.

Last updated 1 years ago

27 stars 2.36 score 35 dependencies

basket - Basket Trial Analysis

Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P. and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.

Last updated 1 years ago

4 stars 1.34 score 68 dependencies

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>

Last updated 4 years ago

clinical-trial-designclinical-trialspreference-design

2 stars 0.96 score 36 dependencies