CMath provides trigonometric and transcendental functions for complex numbers.
CMath is a library that provides trigonometric and transcendental functions for
complex numbers. The functions in this module accept integers, floating-point
numbers or complex numbers as arguments.
Note that the selection of functions is similar, but not identical, to that in
module math. The reason for having two modules is that some users aren't
interested in complex numbers, and perhaps don't even know what they are. They
would rather have Math.sqrt(-1) raise an exception than return a complex number.
pyreadstat is a python package to read and write sas (sas7bdat, sas7bcat,
xport), spps (sav, zsav, por) and stata (dta) data files into/from pandas
dataframes.
GPA Rotation for Factor Analysis
The GPArotation package contains functions for the rotation of factor loadings
matrices. The functions implement Gradient Projection (GP) algorithms for
orthogonal and oblique rotation. Additionally, a number of rotation criteria
are provided. The GP algorithms minimize the rotation criterion function, and
provide the corresponding rotation matrix. For oblique rotation, the
covariance/correlation matrix of the factors is also provided.
The port is required by update of math/R-cran-psych.
Functions are provided for computing the density and the distribution
function of d-dimensional normal and "t" random variables, possibly
truncated (on one side or two sides), and for generating random vectors
sampled from these distributions, except sampling from the truncated
"t". Moments of arbitrary order of a multivariate truncated normal are
computed, and converted to cumulants up to order 4. Probabilities are
computed via non-Monte Carlo methods; different routines are used in the
case d=1, d=2, d=3, d>3, if d denotes the dimensionality.
Required by update of math/R-cran-psych.
The HyperNetX library provides classes and methods for the analysis
and visualization of complex network data modeled as hypergraphs.
The library generalizes traditional graph metrics.
Replace GNU configure to dune builds system.
Add do-test target.
Submitter takes maintainership.
Update WWW.
Switch to DISTVERSION.
The last update of alt-ergo in ports tree was about 10 years ago, the newest
version requires many new libraries in ports tree.
New ports:
devel/ocaml-dolmen
devel/ocaml-gen
devel/ocaml-hmap
devel/ocaml-linol
devel/ocaml-lsp
devel/ocaml-octavius
devel/ocaml-pp_loc
devel/ocaml-ppx_blob
devel/ocaml-ppx_compare
devel/ocaml-ppx_deriving
devel/ocaml-ppx_hash
devel/ocaml-ppx_js_style
devel/ocaml-ppx_yojson_conv
devel/ocaml-ppx_yojson_conv_lib
devel/ocaml-psmt2-frontend
devel/ocaml-trace
math/ocaml-farith
math/ocaml-ocplib-simplex
textproc/ocaml-spelll
PR: 279337
Modin is a drop-in replacement for pandas. While pandas is single-threaded,
Modin lets you instantly speed up your workflows by scaling pandas so it uses
all of your cores. Modin works especially well on larger datasets, where pandas
becomes painfully slow or runs out of memory. Also, Modin comes with the
additional APIs to improve user experience.
Narwhals is an extremely lightweight and extensible compatibility layer between
dataframe libraries!
- Full API support: cuDF, Modin, pandas, Polars, PyArrow
- Interchange-level support: Ibis, Vaex, anything else which implements the
DataFrame Interchange Protocol
Seamlessly support all, without depending on any!
- Just use a subset of the Polars API, no need to learn anything new
- Zero dependencies, Narwhals only uses what the user passes in so your library
can stay lightweight
- Separate lazy and eager APIs, use expressions
- Support pandas' complicated type system and index, without either getting in
the way
- 100% branch coverage, tested against pandas and Polars nightly builds
- Negligible overhead, see overhead
- Let your IDE help you thanks to full static typing, see typing
- Perfect backwards compatibility policy, see stable api for how to opt-in
A calculator that supports various functions such as sqrt, abs, log
sin, cos, tan, asin, acos, atan, rand, round, floor and ceil.
It also allows adjustable output, variables etc
https://github.com/Usbac/quich
PR: 280340
The octave-forge package is the result of The GNU Octave Repositry
project, which is intended to be a central location for custom scripts,
functions and extensions for GNU Octave. contains the source for all the
functions plus build and install scripts.
This is hdf5oct.
MATLAB compatible high-level functions for HDF5 file I/O.
Utility package containing:
1) Facilities for working with grouped data: 'do' something to data
stratified 'by' some variables.
2) LSmeans (least-squares means), general linear estimates.
3) Restrict functions to a smaller domain.
4) Miscellaneous other utilities.
R-based solution for symbolic differentiation. It admits user-defined
function as well as function substitution in arguments of functions
to be differentiated. Some symbolic simplification is part of the
work.
Remark 1: CMake support is experimental. It does not yet support all build
options, but the 64 bits libraries can be installed without conflict with the
classic version.
Remark 2: some tests fail on -CURRENT "SIGSEGV: Segmentation fault -
invalid memory reference.". It seems caused by clang-18 and threads.
Data structures and algorithms for sparse arrays and matrices,
based on index arrays and simple triplet representations,
respectively.
WWW: https://cran.r-project.org/web/packages/slam/
The octave-forge package is the result of The GNU Octave Repositry
project, which is intended to be a central location for custom scripts,
functions and extensions for GNU Octave. contains the source for all
the functions plus build and install scripts.
This is odbc.
Basic Octave implementation of the ODBC toolkit.
pyprobables is a pure-python library for probabilistic data structures. The goal
is to provide the developer with a pure-python implementation of common
probabilistic data-structures to use in their work.
To achieve better raw performance, it is recommended supplying an alternative
hashing algorithm that has been compiled in C. This could include using the MD5
and SHA512 algorithms provided or installing a third party package and writing
your own hashing strategy. Some options include the murmur hash mmh3 or those
from the pyhash library. Each data object in pyprobables makes it easy to pass
in a custom hashing function.
Yuri Victorovich
Rene Ladan
Po-Chuan Hsieh
Stephen Montgomery-Smith
Zsolt Udvari
Wen Heping
wen heping
Benjamin Jacobs
Max Brazhnikov
Tiago Gasiba
Muhammad Moinur Rahman
TAKATSU Tomonari
Thierry Thomas
Nuno Teixeira