Computes a quantile for sorted array of numbers
Degenerate distribution quantile function.
javascript implementation of Dunning's T-Digest for streaming quantile approximation
Normal distribution quantile function.
Beta distribution quantile function.
Gamma distribution quantile function.
Chi-squared distribution quantile function.
Computes a quantile for a numeric array.
Student's t distribution quantile function.
TypeScript implementation of DDSketch, a distributed quantile sketch algorithm
Raised cosine distribution quantile function.
TypeScript definitions for compute-quantile
Inverse gamma distribution quantile function.
F distribution quantile function.
Exponential distribution quantile function.
Binomial distribution quantile function.
Lognormal distribution quantile function.
TypeScript implementation of DDSketch, a distributed quantile sketch algorithm
Poisson distribution quantile function.
Arcsine distribution quantile function.
Uniform distribution quantile function.
Triangular distribution quantile function.
Poisson distribution quantile function.
Cauchy distribution quantile function.
Approximate quantiles using histograms with logarithmically sized bins to guarantee worst case absolute relative error.
Macro that allow to time a function and emit a metric using metrics crate
Macro that allow to time a function and emit a metric using metrics crate
a collection of approximate quantile algorithms
Pure-Rust gradient-boosted trees with quantile regression. First GBT crate with pinball loss, early stopping, and JSON-serializable models.
KLL quantiles sketch from Apache DataSketches for Rust
Zhang-Wang fast quantile algorithm in Rust
Metrics.rs integration for Actix Web
Quantile normalization of a gene x sample count matrix (limma normalizeQuantiles)
Conformal prediction SDK/CLI for neural trading with guaranteed intervals
Time series forecasting library
Metalog probability distribution library and CLI
Graham Cormode and S. Muthukrishnan's Effective Computation of Biased Quantiles over Data Streams in ICDE’05
Implementation of quantile estimators based on. Cormode et. al.: "Effective Computation of Biased Quantiles over Data Streams"
Ruby implementation of Dunning's T-Digest for streaming quantile approximation
DDSketch is a fast-to-insert, fully mergeable, space-efficient quantile sketch with relative error guarantees.
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
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