Global Innovation Through Company Level Data
Researchers and analysts have access to more than 7,500 innovative companies worldwide, which are or have been part of the top 1,000 innovative companies. They can access the six parameters that compose the global IRI Scoreboard's data on R&D: Country, Year, Company's name, Industry, Indicator and Company's rank. Please cite: Warin, Th. (2020) "iiriR: An R Package for the EU Industrial R&D Investment Scoreboard", <doi:10.6084/m9.figshare.11774640.v5>.

person(given = "Thierry",
family = "Warin",
role = c("aut", "cre"),
email = "thierry.warin@hec.ca",
comment = c(ORCID = "0000-0002-5921-3428"))

Fast partial SVD by implicitly-restarted Lanczos
bidiagonalization
A fast and memory-efficient method for computing a few
approximate signular values and singular vectors of large
matrices.

A fast and memory-efficient method for computing a few approximate singular values and singular vectors of large matrices.

A fast and memory-efficient method for computing a few
approximate singular values and singular vectors of large
matrices.

Fast Truncated SVD, PCA and Symmetric Eigendecomposition for
Large Dense and Sparse Matrices
Fast and memory efficient methods for truncated singular and eigenvalue decompositions and principal component analysis of large sparse or dense matrices.

c(
person("Jim", "Baglama", rol=c("aut", "cph"), email="jbaglama@uri.edu"),
person("Lothar", "Reichel", role=c("aut", "cph"), email="reichel@math.kent.edu"),
person("B. W.", "Lewis", role=c("aut","cre","cph"), email="blewis@illposed.net"))
Fast and memory efficient methods for truncated singular and
eigenvalue decompositions and principal component analysis of large sparse or
dense matrices.
Jim Baglama [aut, cph],
Lothar Reichel [aut, cph],
B. W. Lewis [aut, cre, cph]

Fast Truncated Singular Value Decomposition and Principal
Components Analysis for Large Dense and Sparse Matrices
Fast and memory efficient methods for truncated singular value
decomposition and principal components analysis of large sparse and dense matrices.