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#dew

13 packages tagged with “dew”

DewRestClient

A rest client in .net standard

v4.0.2↙ 49.9K
RestClientDew

DewMySQLClientMiddleware

An ASP NET CORE Middleware for MySQL. It works with library DewMySQLClient https://www.nuget.org/packages/DewMySQLClient/

v2.1.1↙ 27.6K
MySQLDewAspnetcoredotnetcore

DewTypes

A set of types for .net standard

v1.5.4↙ 30.7K
.netstandardc#typesstring

DewLocalizationMiddleware

An ASP NET CORE Middleware for site/api localization.

v2.3.0↙ 15.1K
LocalizationDewAspnetcoredotnetcore

DewXamarinLocalization

A localization object to use with xamarin forms on shared project

v1.3.0↙ 10.8K
.netstandardxamarinformsdew

WeatherCalc

A class library to compute - dew point - heat index - windchill - cloud base Eine Klassenbibliothek zur Ermittlung von: - Taupunkt - Hitzeindex - Windchill - Wolkenuntergrenze

v1.2.3↙ 8.5K
weatherdewpointheatindex

Dew.Winform.UI

winform 样式控件

v1.0.1↙ 661
Dewwinform

Dew.Stats.Core

Dew.Stats.Core is the cross-platform and managed-only edition of the Dew.Stats statistical computing library. It provides an extensive suite of tools for probability distributions, hypothesis testing, regression, multivariate analysis, time-series modeling, and experimental design. Built on top of Dew.Math.Core, it operates without native binary dependencies, making it suitable for Windows, Linux, macOS, mobile environments, plugins, sandboxed systems, and WebAssembly deployment. Probability Distributions and Random Models: - PDF, CDF, and inverse CDF for 36+ standard continuous and discrete distributions - Random number generators with parameter estimation and sampling workflows Descriptive and Exploratory Statistics: - Moments, quantiles, percentiles, ranks, scaling, normalization, cumulative transforms - Histograms, ogives, and distribution diagnostics Hypothesis Testing and Statistical Inference: - Parametric tests: t-test, Z-test, F-test, Chi-Squared, Bartlett, Hotelling T² - Non-parametric tests: Wilcoxon, Sign, Mann-Whitney, Anderson–Darling, Shapiro–Wilk, KS - Confidence intervals, residual analysis, goodness-of-fit evaluation Regression and Model Fitting: - Linear and multiple linear regression (weighted / unweighted) - Logistic, Poisson, Ridge regression, nonlinear regression (BFGS / CG / Marquardt / Simplex) - ANOVA and ANCOVA, principal component regression Multivariate and Structure Analysis: - PCA (covariance/correlation), PCA residuals, item analysis, rotational transforms - Classical Multidimensional Scaling and dimensionality significance tests Time Series Analysis and Forecasting: - ACF and PACF, moving-average and smoothing methods - ARMA/ARIMA/ARAR models with coefficient estimation via Yule–Walker, Burg, Innovations, and MLE - Box-Ljung significance tests and forecasting diagnostics Ready-to-Use High-Level Components: - TMtxANOVA, TMtxMulLinReg, TMtxNonLinReg, TMtxPCA, TMtxHypothesisTest, TMtxBinaryTest, TMtxMDScaling Integration Notes: - Uses Dew.Math.Core (managed vector/matrix computation, no native runtime dependency) - No visualization layer is included in the Core edition Dew.Stats.Core is ideal for portable analytics workflows, scripting, services, and embedded scientific computing.

v6.3.10↙ 9.9K
dotnetcsharpcross-platformstatisticsregression

Dew.Stats.Linux

Dew.Stats.Linux is the Linux-native accelerated edition of the Dew.Stats statistical computing library. It provides a comprehensive suite of tools for probability distributions, hypothesis testing, regression, multivariate analysis, experimental design, and time-series modeling, powered by the high-performance vectorized numerical backend in Dew.Math.Linux. This edition is designed for Linux-based compute servers, HPC pipelines, analytics microservices, research clusters, data acquisition systems, and real-time industrial environments. Statistical Capabilities: - Probability distributions (PDF, CDF, inverse CDF) for 36+ discrete and continuous models - Random number generators and parameter estimation - Descriptive statistics, histograms, ogives, quantiles, outlier analysis Hypothesis Testing and Inference: - Parametric tests (t, Z, F, Chi-Squared, Bartlett, Hotelling T²) - Non-parametric tests (Wilcoxon, Sign, Mann–Whitney, Anderson–Darling, Shapiro–Wilk, KS) - Confidence intervals, residual diagnostics, model fitness evaluation Regression and Statistical Modeling: - Linear, multiple linear, logistic, Poisson, ridge and nonlinear regression - ANOVA and ANCOVA - Principal Component Regression and regularization workflows Multivariate and Structural Analysis: - PCA (correlation/covariance) with eigen decomposition - PCA residuals, factor rotation, Bartlett tests, item analysis - Classical Multidimensional Scaling and dimensionality interpretation Time Series Modeling and Forecasting: - ACF and PACF analysis - ARMA, ARIMA and ARAR models - Exponential smoothing (single/double/triple) - Box-Ljung significance testing and forecasting evaluation High-Level Statistical Workflow Components: - TMtxANOVA, TMtxMulLinReg, TMtxNonLinReg, TMtxPCA, TMtxHypothesisTest, TMtxBinaryTest, TMtxMDScaling Platform Characteristics: - Uses **Dew.Math.Linux** for native BLAS/LAPACK acceleration with AVX2/AVX512 dispatch - Highly scalable under multi-threaded workloads - No Windows or WinForms dependencies - Headless execution suitable for batch, service, and compute-node environments Dew.Stats.Linux provides the full analytical capabilities of Dew.Stats, optimized specifically for Linux-based CPU compute environments.

v6.3.10↙ 8.1K
dotnetlinuxstatisticsregressionanova

Dew.Signal.Tee

Dew.Signal.Tee provides high-performance visualization components for the Dew.Signal DSP library, integrating signal analysis pipelines with TeeChart for real-time, interactive charting. Included Components: - **SignalChart**: time-domain waveform visualization with dynamic scaling, cursors, markers, triggers - **SpectrumChart**: frequency-domain visualization with FFT scaling helpers, peak markers, zoom tools - **High-speed series types** optimized for continuous data streams and large capture buffers - **Helpers** for band power measurement, harmonic peak highlighting, dB scaling, and unit conversions Use Cases: - Real-time audio / vibration / RF / instrumentation monitoring - Spectral analysis UIs: FFT, CZT, STFT, spectrogram panels - Oscilloscope-style and waterfall data visualization - Any Windows application requiring fast DSP visualization This package depends on Dew.Signal and Dew.Math.TeePro. It requires WinForms on Windows desktop TFMs. No cross-platform UI here — visualization-only layer.

v6.3.10↙ 2.7K
dotnetcsharpwindowswinformsteechart

Dew.Stats.Tee

Dew.Stats.Tee provides statistical visualization components for Dew.Stats with TeeChart **Pro**. It adds specialized chart types for probability analysis, quality control, distribution insight, and multivariate exploration. Included Visualization Types: - Probability plots (Normal, Weibull, QQ, percentile-based) - Statistical quality control charts: - Variable: X-bar, R, S, EWMA - Attribute: P, NP, U, C - Dot plot, box plot, biplot, error ellipses, scatter / 3D scatter - Histogram, Pareto analysis, error bars, confidence regions Requirements and Packaging: - **Requires TeeChart Pro** (transitively referenced via **Dew.Math.TeePro**) - **TeeChart Lite is not supported** by this package - Windows-only; targets WinForms-enabled .NET TFMs - Visualization is optional; Dew.Stats runs without charting Notes: - Prior to version 6.3.10, these visual components were bundled elsewhere. From 6.3.10 onward, visualization is an opt-in package for clearer modularity.

v6.3.10↙ 2.7K
dotnetcsharpwindowswinformsstatistics

Dew.Stats

Dew.Stats provides a complete high-performance statistical and data analysis library built on the vectorized numerical engine of Dew.Math. It is designed for scientific analytics, quantitative finance, machine learning preprocessing, laboratory data processing, industrial measurement evaluation, and real-time statistical diagnostics. Probability and Random Distributions: - PDF, CDF and inverse CDF for 36+ continuous and discrete probability distributions - Random number generators, parameter estimation, Monte-Carlo sampling, bootstrap methods Descriptive and Exploratory Statistics: - Central moments, percentiles, quantiles, ranks, trimmed means, variance and covariance analysis - Histograms, ogives, cumulative series, outlier detection, normalization, scaling transforms Hypothesis Testing and Inference: - Parametric tests: t-test, Z-test, F-test, Chi-Squared, Bartlett, Hotelling T² - Non-parametric tests: Wilcoxon, Mann-Whitney, Sign, Anderson–Darling, Shapiro–Wilk, KS - Confidence intervals, p-values, power analysis Regression and Statistical Modeling: - Linear, multiple, ridge, logistic, Poisson, and nonlinear regression - ANOVA and ANCOVA models, principal component regression - Robust regression and regularization options Multivariate and Structure Analysis: - PCA (Principal Component Analysis), MDS (Multidimensional Scaling), item analysis, factor modes Time Series Analysis and Forecasting: - Autocorrelation (ACF), partial autocorrelation (PACF), smoothing, ARMA/ARIMA/ARAR models - Rolling statistics, forecasting diagnostics, Box-Ljung significance tests High-Level Components: - TMtxANOVA, TMtxMulLinReg, TMtxNonLinReg, TMtxPCA, TMtxHypothesisTest, TMtxBinaryTest, TMtxMDScaling — encapsulated workflow components for rapid application development Integration: - Uses Dew.Math for optimized vector/matrix operations with AVX2/AVX512 hardware acceleration - Optional charting and statistical visualization available through **Dew.Stats.Tee** (separate package) Dew.Stats is designed for reproducible, numerically stable computation in research, industrial, and engineering applications.

v6.3.10↙ 5.5K
dotnetcsharpstatisticsregressionanova

Dew.Signal

Dew.Signal is a high-performance digital signal processing library built on top of Dew.Math, providing a comprehensive suite of optimized algorithms for real-time signal analysis, filtering, spectral estimation, modeling, and streaming signal workflows. The library is designed for scientific, engineering, audio, RF, vibration, instrumentation, control, and monitoring applications requiring both numerical accuracy and hardware-level performance on multi-core CPUs with AVX2/AVX512 support. Filter Design and Processing: - IIR filter design (Butterworth, Chebyshev I/II, Elliptic, Bessel), analog and digital domains - Order estimation, frequency transformations, bilinear and matched-Z transforms - State-space and zero-pole-numerator-domain modeling with group delay and stability analysis - FIR filter design using window methods and Remez exchange algorithm - FIR/Hilbert/differentiator/integrator design, Savitzky–Golay filtering, envelope detectors - Multi-rate filtering: half-band polyphase filters, decimation, interpolation, zoom-spectrum analysis Nonlinear and Adaptive Filters: - Sample-and-hold, sample-and-decay, and median filtering - High-quality rate conversion with 160dB stopband attenuation at high speed Spectral and Frequency-Domain Analysis: - FFT-based spectrum analyzer components with real-time UI integration support - Parametric spectral estimation: Yule–Walker, Burg, Covariance, Modified Covariance - Chirp-Z transform, bispectrum, bicoherence, transfer function, coherence estimation - Peak interpolation and peak-tracking enhancements, phase unwrapping - Real and complex cepstrum, inverse cepstrum - Spectral statistics: noise floor, SFDR, THD, THDN, SINAD, RMS, SNR measurements Signal Synthesis, Streaming, and Measurement: - Signal generators with stack-based vectorized function evaluation - Audio capture/playback with monitoring and triggering support - Data streaming and file format components for continuous acquisition and logging Forecasting and Time-Series Modeling: - Spectral forecasting based on controlled peak selection, enabling clear component-based prediction Integration and Extensions: - Works seamlessly with Dew.Math numeric structures (vectors/matrices) - Optional high-performance charting available via a separate Dew.Signal.Tee package (Windows visualization) - Part of the Dew Lab Studio ecosystem for unified math, DSP, and statistical analysis workflows Designed for reproducibility, determinism, and stable numerical behavior in long-running or real-time environments.

v6.3.10↙ 5.7K
dotnetcsharpdspsignal-processingfiltering