Showing 145 of total 145 results (show query)

brockk

escalation:A Modular Approach to Dose-Finding Clinical Trials

Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Maintained by Kristian Brock. Last updated 2 months ago.

2.9 match 15 stars 7.91 score 67 scripts

cmclean5

rSpectral:Spectral Modularity Clustering

Implements the network clustering algorithm described in Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity.

Maintained by Anatoly Sorokin. Last updated 2 years ago.

openblascpp

4.3 match 1 stars 3.18 score 9 scripts 1 dependents

bioc

systemPipeShiny:systemPipeShiny: An Interactive Framework for Workflow Management and Visualization

systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community.

Maintained by Le Zhang. Last updated 5 months ago.

shinyappsinfrastructuredataimportsequencingqualitycontrolreportwritingexperimentaldesignclusteringbioconductorbioconductor-packagedata-visualizationshinysystempiper

0.5 match 33 stars 7.03 score 36 scripts

weibzhan

ExplodeLayout:Calculate Exploded Coordinates Based on Original Node Coordinates and Node Clustering Membership

Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual “hairball” that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be “exploded” to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which “explodes” the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl]<https://github.com/UTMB-DIVA-Lab/epl> that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) <PMID: 28815099>). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks.

Maintained by Weibin Zhang. Last updated 3 years ago.

0.5 match 1.70 score 1 scripts