Search Results for "building"

NAVAL-SEM is a fully offline desktop application for PLS-SEM and CB-SEM research. Designed for researchers, PhD scholars, professors, and analysts, NAVAL-SEM provides visual model building, bootstrapping, HTMT, fit indices, mediation analysis, MGA, and export support without requiring cloud access or subscriptions. Key features include: • PLS-SEM and covariance-based SEM • Visual drag-and-drop model builder • Bootstrapping and mediation analysis • HTMT, AVE, CR, Cronbach’s Alpha • Multi-group analysis (MGA) • R/Python/Lavaan export • Fully offline execution for sensitive datasets Available for Windows, macOS, and Linux.

...This library is written in C++ template classes and achieves high speed by exploring the SIMD-instruction-level and thread-level parallelism within Xeon Phis as well as accelerator-level parallelism among multiple Xeon Phis. To facilitate balanced workload distribution, we have proposed a general framework for symmetric all-pairs computation by building provable bijective functions between job identifier and coordinate space for the first time.

...Main object is a sparse bit string (Bits) that efficiently scales up to 2^63 bits normally compressed as forest has duplicated substrings. Bits objects support reading bit, byte, short, int, or long (Java primitives) at any bit index in 64 bit range. Example: instead of building a class to hold a header and then data, represent all of that as Bits, subranges of them, and ints for sizes of its parts. Expansion ability for other kinds of compression, since Bits is a Java interface. Main functions on bits are substring, concat, number of 0 or 1 bits, and number of bits (size). All those operations can be done millions of times per second regardless of size because the AVL forest reuses existing branches recursively. ...

...KeplerDB is dedicated to be scalable and to create clusters of server allowing the user to analyse and store massing amount of data to monitor systems like computers, clusters, building and captors or financial systems like markets and accounts. The user can use KeplerDB to make data analysis on enormous amount of data (statistics and modelling).

The procedure of building the environment of Emacs + ESS for R is more complicated, so we exploit the automated installation software that combines R, Emacs and ESS to let the the public download and install.

...It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model.