Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.
BreastSubtypeR is an R package that provides a collection of methods for intrinsic molecular subtyping of breast cancer. It includes subtyping methods for nearest centroid-based subtyping (NC-based) and single sample predictor (SSP-based), along with tools for integrating clinical data and visualizing results.
This package provides a collection of Hi-C files (pairs, (m)cool and fastq). These datasets can be read into R and further investigated and visualized with the HiContacts package. Data includes yeast Hi-C data generated by the Koszul lab from the Pasteur Institute.
The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.
The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.
The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.
The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.
The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.
The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.
The windows crate lets you call any Windows API past, present, and future using code generated on the fly directly from the metadata describing the API and right into your Rust package where you can call them as if they were just another Rust module.
Turbo aims to be as fast as single-page web application without having to write any JavaScript. Turbo accelerates links and form submissions without requiring server-side changes to the generated HTML. It allows carving up a page into independent frames, which can be lazy-loaded and operated as independent components. Finally, it helps making partial page updates using just HTML and a set of CRUD-like container tags. These three techniques reduce the amount of custom JavaScript that many web applications need to write by an order of magnitude. And for the few dynamic bits that are left, Stimulus can be used.
Offers a handful of useful wrapper functions which streamline the reading, analyzing, and visualizing of variant call format (vcf) files in R. This package was designed to facilitate an explicit pipeline for optimizing Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>) parameters during de novo (without a reference genome) assembly and variant calling of restriction-enzyme associated DNA sequence (RADseq) data. The pipeline implemented here is based on the 2017 paper "Lost in Parameter Space" (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which establishes clear recommendations for optimizing the parameters m', M', and n', during the process of assembling loci.
Easily compute an aggregate ranking (also called a median ranking or a consensus ranking) according to the axiomatic approach presented by Cook et al. (2007). This approach minimises the number of violations between all candidate consensus rankings and all input (partial) rankings, and draws on a branch and bound algorithm and a heuristic algorithm to drastically improve speed. The package also provides an option to bootstrap a consensus ranking based on resampling input rankings (with replacement). Input rankings can be either incomplete (partial) or complete. Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.
Provide addins for RStudio'. It currently contains 3 addins. The first to add a shortcut for the double pipe. The second is to add a shortcut for the same operator. And the third to simplify the creation of vectors from texts pasted from the computer transfer area.
It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).
Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the Efftox (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding.
This package provides a simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.
The Patient Rule Induction Method (PRIM) is typically used for "bump hunting" data mining to identify regions with abnormally high concentrations of data with large or small values. This package extends this methodology so that it can be applied to binary classification problems and used for prediction.
Visual representations of model fit or predictive success in the form of "separation plots." See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002.
The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference.
This package provides a cross between a 2D density plot and a scatter plot, implemented as a ggplot2 geom. Points in the scatter plot are colored by the number of neighboring points. This is useful to visualize the 2D-distribution of points in case of overplotting.
This package provides a collection of utilities that allow programming with R's operators. Routines allow classifying operators, translating to and from an operator and its underlying function, and inverting some operators (e.g. comparison operators), etc. All methods can be extended to custom infix operators.
Bars, in the present context, are lines above and below text that abut with the text. Barred roman numerals are sometimes found in publications. The package provides a function that prints barred roman numerals (converting Arabic numerals if necessary). The package also provides a predicate \ifnumeric.