The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

The "clang-runtime" library provides the implementations of run-time functions for C and C++ programs. It also provides header files that allow C and C++ source code to interface with the "sanitization" passes of the clang compiler. In LLVM this library is called "compiler-rt".

Sorcerer generates Ruby code from a Ripper-like abstract syntax tree (i.e. S-Expressions). Sorcerer is targeted mainly at small snippets of Ruby code, expressible in a single line. Longer examples may be re-sourced, but they will be rendered in a single-line format.

Net::SFTP is a pure Ruby implementation of the SFTP protocol (specifically, versions 1 through 6 of the SFTP protocol). Note that this is the “Secure File Transfer Protocol”, typically run over an SSH connection, and has nothing to do with the FTP protocol.

These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.

Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.

Around 10% of almost any predictive modeling project is spent in predictive modeling, funModeling and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables dataViz', assessing model performance and other functions.

Splits date and time of day components from continuous datetime objects, then plots them using grammar of graphics ('ggplot2'). Plots can also be decorated with solar cycle information (e.g., sunset, sunrise, etc.). This is useful for visualising data that are associated with the solar cycle.

Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in Martinez-de-Pison (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.

Analyse and visualise multi electrode array data at the single electrode and whole well level, downstream of AxIS Navigator 3.6.2 Software processing. Compare bursting parameters between time intervals and recordings using the bar chart visualisation functions. Compatible with 12- and 24- well plates.

The package is used for calibrating the design parameters for single-to-double arm transition design proposed by Shi and Yin (2017). The calibration is performed via numerical enumeration to find the optimal design that satisfies the constraints on the type I and II error rates.

This package provides a step-down procedure for controlling the False Discovery Proportion (FDP) in a competition-based setup, implementing Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression.

This package provides functions to generate or sample from all possible splits of features or variables into a number of specified groups. Also computes the best split selection estimator (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

Total variation denoising can be used to approximate a given sequence of noisy observations by a piecewise constant sequence, with adaptively-chosen break points. An efficient linear-time algorithm for total variation denoising is provided here, based on Johnson (2013) <doi:10.1080/10618600.2012.681238>.

This package implements the Whale Optimization Algorithm(WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. The methodology is based on "The Whale Optimization Algorithm" by Mirjalili and Lewis (2016) <doi:10.1016/j.advengsoft.2016.01.008>.

DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It models the expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles.