Search Results for "statistical"

DROP implements the model libraries and provides systems for fixed income valuation and adjustments, asset allocation and transaction cost analytics, and supporting libraries in numerical optimization and statistical learning. DROP is composed of four main libraries: [Asset Allocation] (https://lakshmidrip.github.io/DROP/AssetAllocationModule.html) [Fixed Income Analytics] (https://lakshmidrip.github.io/DROP/FixedIncomeModule.html) [Numerical Optimization] (https://lakshmidrip.github.io/DROP/NumericalOptimizerModule.html) [Statistical Learning] (https://lakshmidrip.github.io/DROP/StatisticalLearningModule.html)

SADSA (Software Application for Data Science and Analytics) is a Python-based desktop application designed to simplify statistical analysis, machine learning, and data visualization for students, researchers, and data professionals. Built using Python for the GUI, SADSA provides a menu-driven interface for handling datasets, applying transformations, running advanced statistical tests, machine learning algorithms, and generating insightful plots — all without writing code.

HotelDruid is a property management system (PMS) designed to make hotel and hostel rooms, bed and breakfast apartments, or any other kind of daily rental easy to manage from a web browser.

Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc.

This library allows you to generate a random number in the specified range from 0 to n, without using the built-in function Math.random (), and gets a random number from the processing time of polymorphic mathematical calculations, which depends on the current physical parameters of the CPU, RAM, and t .P. The resulting sequence corresponds to a discrete uniform distribution and is close to natural random sequences in terms of statistical randomness tests (LFSR, Approximate Entropy, DIEHARD Test).