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Wolfram Mathematica: C++, Wolfram Language 14.1.0 (July 31, 2024; 5 months ago (3] No Yes Proprietary Constrained nonlinear optimization, interior point methods, convex optimization and integer programming-as well as original symbolic methods integrated with general computational capabilities. MIDACO
Wolfram Mathematica is a software system with built-in libraries for several areas of technical computing that allows machine learning, statistics, symbolic computation, data manipulation, network analysis, time series analysis, NLP, optimization, plotting functions and various types of data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in ...
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Vector graphics software can be used for manual graphing or for editing the output of another program. Please see: Category:Vector graphics editors; Comparison of vector graphics editors
In 1992, the Macintosh version of Statistica was released. Statistica 5.0 was released in 1995. It ran on both the new 32-bit Windows 95/NT and the previous 16-bit version, Windows 3.1. It featured many new statistics and graphics procedures, a word-processor-style output editor (combining tables and graphs), and a built-in development ...
Figure 1. Probabilistic parameters of a hidden Markov model (example) X — states y — possible observations a — state transition probabilities b — output probabilities. In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). [7]
Mathematica: the built-in function SurvivalModelFit creates survival models. [16] SAS: The Kaplan–Meier estimator is implemented in the proc lifetest procedure. [17] R: the Kaplan–Meier estimator is available as part of the survival package. [18] [19] [20] Stata: the command sts returns the Kaplan–Meier estimator. [21] [22]
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.