This library was designed to make it easier to access web services that claim to be "RESTful". It includes convenience wrappers for libsoup and libxml to ease remote use of the RESTful API.

This library was designed to make it easier to access web services that claim to be "RESTful". It includes convenience wrappers for libsoup and libxml to ease remote use of the RESTful API.

Restic is a program that does backups right and was designed with the following principles in mind:

• Easy: Doing backups should be a frictionless process, otherwise you might be tempted to skip it. Restic should be easy to configure and use, so that, in the event of a data loss, you can just restore it. Likewise, restoring data should not be complicated.

• Fast: Backing up your data with restic should only be limited by your network or hard disk bandwidth so that you can backup your files every day. Nobody does backups if it takes too much time. Restoring backups should only transfer data that is needed for the files that are to be restored, so that this process is also fast.

• Verifiable: Much more important than backup is restore, so restic enables you to easily verify that all data can be restored.

• Secure: Restic uses cryptography to guarantee confidentiality and integrity of your data. The location the backup data is stored is assumed not to be a trusted environment (e.g. a shared space where others like system administrators are able to access your backups). Restic is built to secure your data against such attackers.

• Efficient: With the growth of data, additional snapshots should only take the storage of the actual increment. Even more, duplicate data should be de-duplicated before it is actually written to the storage back end to save precious backup space.

Restic is a program that does backups right and was designed with the following principles in mind:

• Easy: Doing backups should be a frictionless process, otherwise you might be tempted to skip it. Restic should be easy to configure and use, so that, in the event of a data loss, you can just restore it. Likewise, restoring data should not be complicated.

• Fast: Backing up your data with restic should only be limited by your network or hard disk bandwidth so that you can backup your files every day. Nobody does backups if it takes too much time. Restoring backups should only transfer data that is needed for the files that are to be restored, so that this process is also fast.

• Verifiable: Much more important than backup is restore, so restic enables you to easily verify that all data can be restored.

• Secure: Restic uses cryptography to guarantee confidentiality and integrity of your data. The location the backup data is stored is assumed not to be a trusted environment (e.g. a shared space where others like system administrators are able to access your backups). Restic is built to secure your data against such attackers.

• Efficient: With the growth of data, additional snapshots should only take the storage of the actual increment. Even more, duplicate data should be de-duplicated before it is actually written to the storage back end to save precious backup space.

Restbed is a comprehensive and consistent programming model for building applications that require seamless and secure communication over HTTP.

Implementation of the RESTK algorithm based on Markov's Inequality from Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. <doi:10.4230/LIPIcs.ECRTS.2022.20>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

RESTinio is a header-only C++14 library that gives you an embedded HTTP/Websocket server. It is based on standalone version of ASIO and targeted primarily for asynchronous processing of HTTP-requests.

RESTinio is a header-only C++14 library that gives you an embedded HTTP/Websocket server. It is based on standalone version of ASIO and targeted primarily for asynchronous processing of HTTP-requests.

Download large sections of GenBank <https://www.ncbi.nlm.nih.gov/genbank/> and generate a local SQL-based database. A user can then query this database using restez functions or through rentrez <https://CRAN.R-project.org/package=rentrez> wrappers.

This package provides a daemon for checking running and not running processes. It reads the /proc directory every n seconds and does a POSIX regexp on the process names. The daemon runs a user-provided script when it detects a program in the running processes, or an alternate script if it doesn't detect the program. The daemon can only be called by the root user, but can use sudo -u user in the process called if needed.

RESTAS is a Common Lisp web application framework.

RESTAS is a Common Lisp web application framework.

This package models a RESTful service as if it were a nested R list.

This package provides a RESTful API wrapper for accessing the GENESIS database of the German Federal Statistical Office (Destatis) as well as its Census Database and the database of Germany's regional statistics. Supports data search functions, credential management, result caching, and handling remote background jobs for large datasets.

Flexible framework for ecological restoration planning. It aims to identify priority areas for restoration efforts using optimization algorithms (based on Justeau-Allaire et al. 2021 <doi:10.1111/1365-2664.13803>). Priority areas can be identified by maximizing landscape indices, such as the effective mesh size (Jaeger 2000 <doi:10.1023/A:1008129329289>), or the integral index of connectivity (Pascual-Hortal & Saura 2006 <doi:10.1007/s10980-006-0013-z>). Additionally, constraints can be used to ensure that priority areas exhibit particular characteristics (e.g., ensure that particular places are not selected for restoration, ensure that priority areas form a single contiguous network). Furthermore, multiple near-optimal solutions can be generated to explore multiple options in restoration planning. The package leverages the Choco-solver software to perform optimization using constraint programming (CP) techniques (<https://choco-solver.org/>).

RESTAS is a Common Lisp web application framework.

Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/web/main/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services (<https://wikis.ec.europa.eu/pages/viewpage.action?pageId=44165555>) to search and download data from the Eurostat database using the SDMX standard.

Allow for easy-to-use testing or evaluating of linear equality and inequality restrictions about parameters and effects in (generalized) linear statistical models.

RestRserve is an R web API framework for building high-performance AND robust microservices and app backends. With Rserve backend on UNIX-like systems it is parallel by design. It will handle incoming requests in parallel - each request in a separate fork.

Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.

This package provides a random-effects stochastic model that allows quick detection of clonal dominance events from clonal tracking data collected in gene therapy studies. Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. Although this assumption makes inference easier, in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. Therefore we extended the base model by introducing random effects for the clones. In this extended formulation the dynamic parameters are estimated using a tailor-made expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.05.31.494100>.

This package provides a FFI-based Lua API for ngx_http_lua_module or ngx_stream_lua_module.

This package provides the user with functions to develop their trading strategy, uncover actionable trading ideas, and monitor consensus shifts with crowdsourced earnings and economic estimate data directly from <www.estimize.com>. Further information regarding the web services this package invokes can be found at <www.estimize.com/api>.

Time Series Segmented Residual Trends is a method for the automated detection of land degradation from remotely sensed vegetation and climate datasets. TSS-RESTREND incorporates aspects of two existing degradation detection methods: RESTREND which is used to control for climate variability, and BFAST which is used to look for structural changes in the ecosystem. The full details of the testing and justification of the TSS-RESTREND method (version 0.1.02) are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>. The changes to the method introduced in version 0.2.03 focus on the inclusion of temperature as an additional climate variable. This allows for land degradation assessment in temperature limited drylands. A paper that details this work is currently under review. There are also a number of bug fixes and speed improvements. Version 0.3.0 introduces additional attribution for eCO2, climate change and climate variability the details of which are in press in Burrell et al., (2020). The version under active development and additional example scripts showing how the package can be applied can be found at <https://github.com/ArdenB/TSSRESTREND>.