Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls' and 'widekernelpls') with parity to 'pls'. Optimized for 'bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) &lt;<a href="https://hal.science/hal-05352069" target="_top">https://hal.science/hal-05352069</a>&gt;, and &lt;<a href="https://hal.science/hal-05352061" target="_top">https://hal.science/hal-05352061</a>&gt; highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) &lt;<a href="https://doi.org/10.1002/(SICI)1099-128X(199701)11:1%3C73::AID-CEM435%3E3.0.CO;2-%23" target="_top">doi:10.1002/(SICI)1099-128X(199701)11:1%3C73::AID-CEM435%3E3.0.CO;2-%23</a>&gt;, Rosipal &amp; Trejo (2001) &lt;<a href="https://www.jmlr.org/papers/v2/rosipal01a.html" target="_top">https://www.jmlr.org/papers/v2/rosipal01a.html</a>&gt;, Tenenhaus, Viennet, and Saporta (2007) &lt;<a href="https://doi.org/10.1016%2Fj.csda.2007.01.004" target="_top">doi:10.1016/j.csda.2007.01.004</a>&gt;, Rosipal (2004) &lt;<a href="https://doi.org/10.1007%2F978-3-540-45167-9_17" target="_top">doi:10.1007/978-3-540-45167-9_17</a>&gt;, Rosipal (2019) &lt;<a href="https://ieeexplore.ieee.org/document/8616346" target="_top">https://ieeexplore.ieee.org/document/8616346</a>&gt;, Song, Wang, and Bai (2024) &lt;<a href="https://doi.org/10.1016%2Fj.chemolab.2024.105238" target="_top">doi:10.1016/j.chemolab.2024.105238</a>&gt;. Includes kernel logistic PLS with 'C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates.

Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) &lt;<a href="https://hal.science/hal-05352069" target="_top">https://hal.science/hal-05352069</a>&gt;, and &lt;<a href="https://hal.science/hal-05352061" target="_top">https://hal.science/hal-05352061</a>&gt; highlighted fitting and cross-validating PLS-based Cox models to censored big data.

High performance principal component analysis routines that operate directly on 'bigmemory::big.matrix' objects. The package avoids materialising large matrices in memory by streaming data through 'BLAS' and 'LAPACK' kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into 'bigmemory'-backed matrices for file-based workflows. Additional interfaces expose 'scalable' singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. 'Scalable' principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) &lt;<a href="https://doi.org/10.1145%2F2723372.2751520" target="_top">doi:10.1145/2723372.2751520</a>&gt;.

Manipulate data through memory-mapped files, as vectors, matrices or arrays. Basic arithmetic functions are implemented, but currently no matrix arithmetic. Can write and read descriptor files for compatibility with the 'bigmemory' package.

𝗶𝘁𝗲𝗹 𝗔𝟭𝟬𝟬𝗖 – Thiết kế tinh tế, hiệu năng ổn định cho cuộc sống năng động 🌿📱 

#itel #itelA100C #Smartphone #BigMemory #VibrantStyle #CôngNghệ #HiệuNăng #PhongCách 1️⃣ Thiết kế thời thượng, phong cách hiện đại 

itel A100C mang ngôn ngữ thiết kế tinh giản nhưng không kém phần sang trọng. Các đường bo mềm mại cùng lớp hoàn thiện mờ giúp máy toát lên vẻ thanh lịch, dễ cầm nắm và phù hợp với nhiều phong cách khác nhau.

𝗶𝘁𝗲𝗹 𝗔𝟭𝟬𝟬𝗖 – Thiết kế tinh tế, hiệu năng ổn định cho cuộc sống năng động 🌿📱 

#itel #itelA100C #Smartphone #BigMemory #VibrantStyle #CôngNghệ #HiệuNăng #PhongCách 1️⃣ Thiết kế thời thượng, phong cách hiện đại 

itel A100C mang ngôn ngữ thiết kế tinh giản nhưng không kém phần sang trọng. Các đường bo mềm mại cùng lớp hoàn thiện mờ giúp máy toát lên vẻ thanh lịch, dễ cầm nắm và phù hợp với nhiều phong cách khác nhau. Phần cụm camera sau được xử lý hài hòa, mang lại điểm nhấn độc đáo mà vẫn giữ tổng thể gọn gàng.

Exact nearest-neighbour and radius-search routines that operate directly on 'bigmemory::big.matrix' objects. The package streams row blocks through 'BLAS' kernels, supports self-search and external-query search, exposes prepared references for repeated queries, and can build exact k-nearest-neighbour, radius, mutual k-nearest-neighbour, and shared-nearest-neighbour graphs. Version 0.3.0 adds execution plans, serializable prepared caches, resumable streamed graph jobs, coercion helpers, exact candidate reranking, and recall summaries for evaluating approximate neighbours.

Approximate Euclidean k-nearest neighbour search routines that operate on 'bigmemory::big.matrix' data through Annoy indexes created with 'RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar metadata from streamed 'big.matrix' rows, supports euclidean, angular, Manhattan, and dot-product Annoy metrics, and can either return in-memory results or stream neighbour indices and distances into destination 'bigmemory' matrices. Explicit index life cycle helpers, stronger metadata validation, descriptor-aware file-backed workflows, and benchmark helpers are also included.

𝗶𝘁𝗲𝗹 𝗔𝟭𝟬𝟬𝗖 – Thiết kế tinh tế, hiệu năng ổn định cho cuộc sống năng động 🌿📱 

#itel #itelA100C #Smartphone #BigMemory #VibrantStyle #CôngNghệ #HiệuNăng #PhongCách1️⃣ Thiết kế thời thượng, phong cách hiện đại 

itel A100C mang ngôn ngữ thiết kế tinh giản nhưng không kém phần sang trọng. Các đường bo mềm mại cùng lớp hoàn thiện mờ giúp máy toát lên vẻ thanh lịch, dễ cầm nắm và phù hợp với nhiều phong cách khác nhau.

Hello! I&#039;m here to help you with any questions or tasks you might have. Whether you need information, assistance with a project, or just want to chat, feel free to let me know how I can assist you! 😊 𝗶𝘁𝗲𝗹 𝗔𝟭𝟬𝟬𝗖 – Thiết kế tinh tế, hiệu năng ổn định cho cuộc sống năng động 🌿📱 

#itel #itelA100C #Smartphone #BigMemory #VibrantStyle #CôngNghệ #HiệuNăng #PhongCách&hellip;