Tor protects you by bouncing your communications around a distributed network of relays run by volunteers all around the world: it prevents somebody watching your Internet connection from learning what sites you visit, and it prevents the sites you visit from learning your physical location. Tor works with many of your existing applications, including web browsers, instant messaging clients, remote login, and other applications based on the TCP protocol.

This package is the full featured tor which is needed for running relays, bridges or directory authorities. If you just want to access the Tor network or to setup an onion service you may install tor-client instead.

The goal of tor (to-R) is to help you to import multiple files from a single directory at once, and to do so as quickly, flexibly, and simply as possible.

The package is based around `torus mapping', which is a non-perturbative technique for creating orbital tori for specified values of the action integrals. Given an orbital torus and a star's position at a reference time, one can compute its position at any other time, no matter how remote.

TORCS stands for The Open Racing Car Simulator. It can be used as an ordinary car racing game, as an artificial intelligence (AI) racing game, or as a research platform. The game has features such as:

• Input support for a driving wheel, joystick, keyboard or mouse

• More than 30 car models

• 30 tracks

• 50 opponents to race against

• Lighting, smoke, skidmarks and glowing brake disks graphics

• Simple damage model and collisions

• Tire and wheel properties (springs, dampers, stiffness, etc.)

• Aerodynamics (ground effect, spoilers, etc.)

The difficulty level can be configured, impacting how much damage is caused by collisions and the level of traction the car has on the track, which makes the game fun for both novice and experts.

This package provides functionality to define and train neural networks similar to PyTorch but written entirely in R using the libtorch library. It also supports low-level tensor operations and GPU acceleration.

Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI:10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)<DOI:10.1080/02664763.2020.1864817>." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). <DOI:10.1214/aoms/1177704374>. "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model.

Torsocks allows you to use most applications in a safe way with Tor. It ensures that DNS requests are handled safely and explicitly rejects UDP traffic from the application you're using.

Draws tornado plots for model sensitivity to univariate changes. Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package. Also draws variable importance plots.

Tor Browser is the Tor Project version of Firefox browser. It is the only recommended way to anonymously browse the web that is supported by the project. It modifies Firefox in order to avoid many known application level attacks on the privacy of Tor users.

Tor protects you by bouncing your communications around a distributed network of relays run by volunteers all around the world: it prevents somebody watching your Internet connection from learning what sites you visit, and it prevents the sites you visit from learning your physical location. Tor works with many of your existing applications, including web browsers, instant messaging clients, remote login, and other applications based on the TCP protocol.

To torify applications (to take measures to ensure that an application, which has not been designed for use with Tor such as ssh, will use only Tor for internet connectivity, and also ensures that there are no leaks from DNS, UDP or the application layer) you need to install torsocks.

This package only provides a client to the Tor Network.

Optimizers for torch deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) adabelief by Zhuang et al (2020), <arXiv:2010.07468>; (b) adabound by Luo et al.(2019), <arXiv:1902.09843>; (c) adahessian by Yao et al.(2021) <arXiv:2006.00719>; (d) adamw by Loshchilov & Hutter (2019), <arXiv:1711.05101>; (e) madgrad by Defazio and Jelassi (2021), <arXiv:2101.11075>; (f) nadam by Dozat (2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) qhadam by Ma and Yarats(2019), <arXiv:1810.06801>; (h) radam by Liu et al. (2019), <arXiv:1908.03265>; (i) swats by Shekar and Sochee (2018), <arXiv:1712.07628>; (j) yogi by Zaheer et al.(2019), <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.

Documentation at https://melpa.org/#/torus

This library provides support for parsing and generating BitTorrent files.

This package provides facilities for working with .torrent or metainfo files. Implements a bencode reader and writer according to Bitorrent BEP003.

This package provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the torch package and it's API borrows heavily from PyTorch vision package.

Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed. By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user.

Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed. By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user.

This package provides datasets in a format that can be easily consumed by torch dataloaders'. Handles data downloading from multiple sources, caching and pre-processing so users can focus only on their model implementations.

This package enables you to deserialize Lua torch-serialized objects from Python.

This package implements additional operators for computer vision models, including operators necessary for image segmentation and object detection deep learning models.

Documentation at https://melpa.org/#/torrent-mode

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

This tool provides ordinary differential equation solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost.

image and video datasets and models for torch deep learning