Showing 5 of total 5 results (show query)
mlr-org
paradox:Define and Work with Parameter Spaces for Complex Algorithms
Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.
Maintained by Martin Binder. Last updated 9 months ago.
experimental-designhyperparametersmlr3transformations
29 stars 11.56 score 316 scripts 38 dependents
openpharma
crmPack:Object-Oriented Implementation of CRM Designs
Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Maintained by Daniel Sabanes Bove. Last updated 3 months ago.
21 stars 7.76 score 208 scripts
erhard-lab
grandR:Comprehensive Analysis of Nucleotide Conversion Sequencing Data
Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, (see 'Jürges et al' <doi:10.1093/bioinformatics/bty256>) and specialized tools for downstream analyses. 'grandR' provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.
Maintained by Florian Erhard. Last updated 2 months ago.
11 stars 7.03 score 18 scripts 1 dependents
gpaux
Mediana:Clinical Trial Simulations
Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.
Maintained by Gautier Paux. Last updated 4 years ago.
biostatisticsclinical-trial-simulationsclinical-trialssimulations
29 stars 6.53 score 39 scripts
iame-researchcenter
PFIM:Population Fisher Information Matrix
Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>, Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>, Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>, Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>, Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.
Maintained by Romain Leroux. Last updated 5 months ago.
2.78 score 9 scripts