R-universe search: topic:stochastic-gradient-descent

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bioc

BEclear:Correction of batch effects in DNA methylation data

Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.

Maintained by Livia Rasp. Last updated 5 months ago.

batcheffect dnamethylation software preprocessing statisticalmethod batch-effects bioconductor-package dna-methylation latent-factor-model methylation missing-data missing-values stochastic-gradient-descent cpp

4 stars 5.90 score 11 scripts

sgdinference-lab

SGDinference:Inference with Stochastic Gradient Descent

Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the SGDinference package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <arXiv:2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".

Maintained by Youngki Shin. Last updated 1 years ago.

inference sgd stochastic-gradient-descent subgradient openblas cpp

1 stars 3.70 score 4 scripts