### Software

- Beta versions of Matlab routines for principal component analysis:
beta.tar.gz,
beta.tar.
- Python routines for principal component analysis
("pip install fbpca" also works):
fbpca.py.
- A Fortran software distribution for the low-rank approximation of matrices:
id_dist.4.tar.gz,
id_dist.4.tar.
- Documentation for the above Fortran software
(the above distribution also contains this documentation):
id_doc.4.pdf.
- Matlab tests accompanying the paper,
"Accurate low-rank approximations via a few iterations
of alternating least squares":
als.tar.gz,
als.tar.
- Python tests accompanying the paper,
"Randomized algorithms for distributed computation
of principal component analysis and singular value decomposition":
valid.tar.gz.
- PyTorch codes accompanying the paper,
"Compressed sensing with a jackknife and a bootstrap":
GitHub.
(Jure Zbontar wrote substantial parts of this software.)
- PyTorch codes accompanying the paper,
"Secure multiparty computations in floating-point arithmetic":
GitHub.
(Awni Hannun wrote substantial parts of this software.)
- Python codes accompanying the paper,
"Cumulative deviation of a subpopulation from the full population":
GitHub.
- Python codes accompanying the paper,
"A graphical method of cumulative differences between two subpopulations":
GitHub.
- Python codes accompanying the paper,
"Controlling for multiple covariates":
GitHub.
- Python codes accompanying the paper,
"Calibration of P-values for calibration and for deviation of a subpopulation
from the full population":
GitHub.
- Python codes accompanying the paper,
"Metrics of calibration for probabilistic predictions":
GitHub.
- C and Python codes accompanying the paper,
"An efficient algorithm for integer lattice reduction":
GitHub.
- Python codes accompanying the paper,
"Cumulative differences between paired samples":
GitHub.