Tar provides the ability to create tar archives, as well as the ability to extract, update or list files in an existing archive. It is useful for combining many files into one larger file, while maintaining directory structure and file information such as permissions and creation/modification dates. GNU tar offers many extensions over the standard utility.

Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.

Tarlz is a massively parallel (multi-threaded) combined implementation of the tar archiver and the lzip compressor. Tarlz creates, lists, and extracts archives in a simplified and safer variant of the POSIX pax format compressed with lzip, keeping the alignment between tar members and lzip members. The resulting multimember tar.lz archive is fully backward compatible with standard tar tools like GNU tar, which treat it like any other tar.lz archive. Tarlz can append files to the end of such compressed archives.

cl-tar is a Common Lisp library providing a high-level interface for interacting with tar archives.

cl-tar is a Common Lisp library providing a high-level interface for interacting with tar archives.

This library is for working with \".tar\" archive files. It can read and write a range of common variations of the tar archive format including V7, POSIX USTAR and GNU formats. It provides support for packing and unpacking portable archives. This makes it suitable for distribution but not backup because details like file ownership and exact permissions are not preserved. It also provides features for random access to archive content using an index.

tar for node

cl-tar is a Common Lisp library providing a high-level interface for interacting with tar archives.

Measure text's sentiment with dictionaries and simple rules covering negations and modifiers. User-supplied dictionaries are supported, including Unicode emojis and multi-word tokens, so this package can also be used to study constructs beyond sentiment.

Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) <doi: 10.1038/nprot.2013.150>. Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data.

This package provides a Rust implementation of a TAR file reader and writer. This library does not currently handle compression, but it is abstract over all I/O readers and writers. Additionally, great lengths are taken to ensure that the entire contents are never required to be entirely resident in memory all at once.

Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.

This package provides a pipeline toolkit for statistics and data science in R; the targets package brings function-oriented programming to Make-like declarative pipelines. It orchestrates a pipeline as a graph of dependencies, skips steps that are already up to date, runs the necessary computation with optional parallel workers, abstracts files as R objects, and provides tangible evidence that the results are reproducible given the underlying code and data. The methodology in this package borrows from GNU Make (2015, ISBN:978-9881443519) and drake (2018, <doi:10.21105/joss.00550>).

This package provides raw files recorded on different Liquid Chromatography Mass Spectrometry (LC-MS) instruments. All included MS instruments are manufactured by Thermo Fisher Scientific and belong to the Orbitrap Tribrid or Q Exactive Orbitrap family of instruments. Despite their common origin and shared hardware components, e.g., Orbitrap mass analyser, the above instruments tend to write data in different "dialects" in a shared binary file format (.raw). The intention behind tartare is to provide complex but slim real-world files that can be used to make code robust with respect to this diversity. In other words, it is intended for enhanced unit testing. The package is considered to be used with the rawrr package and the Spectra MsBackends.

Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Get information on compilation target.

This package provides extensione for diagnostic error handling with `miette`.

Archive huge numbers of files, or split massive tar archives into smaller chunks.

cl-tar-file is a Common Lisp library that allows reading from and writing to various tar archive formats. Currently supported are the POSIX ustar, PAX (ustar with a few new entry types), GNU, and v7 (very old) formats.

This library is rather low level and is focused exclusively on reading and writing physical tar file entries using streams. Therefore, it contains no functionality for automatically building archives from a set of files on the filesystem or writing the contents of a file to the filesystem. Additionally, there are no smarts that read multiple physical entries and combine them into a single logical entry (e.g., with PAX extended headers or GNU long link/path name support). For a higher-level library that reads and writes logical entries, and also includes filesystem integration, see cl-tar.

cl-tar-file is a Common Lisp library that allows reading from and writing to various tar archive formats. Currently supported are the POSIX ustar, PAX (ustar with a few new entry types), GNU, and v7 (very old) formats.

This library is rather low level and is focused exclusively on reading and writing physical tar file entries using streams. Therefore, it contains no functionality for automatically building archives from a set of files on the filesystem or writing the contents of a file to the filesystem. Additionally, there are no smarts that read multiple physical entries and combine them into a single logical entry (e.g., with PAX extended headers or GNU long link/path name support). For a higher-level library that reads and writes logical entries, and also includes filesystem integration, see cl-tar.

Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features.

Function-oriented Make-like declarative pipelines for statistics and data science are supported in the targets R package. As an extension to targets, the tarchetypes package provides convenient user-side functions to make targets easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the drake R package by Will Landau (2018) <doi:10.21105/joss.00550>.

cl-tar-file is a Common Lisp library that allows reading from and writing to various tar archive formats. Currently supported are the POSIX ustar, PAX (ustar with a few new entry types), GNU, and v7 (very old) formats.

This library is rather low level and is focused exclusively on reading and writing physical tar file entries using streams. Therefore, it contains no functionality for automatically building archives from a set of files on the filesystem or writing the contents of a file to the filesystem. Additionally, there are no smarts that read multiple physical entries and combine them into a single logical entry (e.g., with PAX extended headers or GNU long link/path name support). For a higher-level library that reads and writes logical entries, and also includes filesystem integration, see cl-tar.

This package provides a first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method.