Utilities to read and write files in the FITS (Flexible
Image Transport System) format, a standard format in astronomy (see
e.g. <https://en.wikipedia.org/wiki/FITS> for more information).
Present low-level routines allow: reading, parsing, and modifying
FITS headers; reading FITS images (multi-dimensional arrays);
reading FITS binary and ASCII tables; and writing FITS images
(multi-dimensional arrays).  Higher-level functions allow: reading
files composed of one or more headers and a single (perhaps
multidimensional) image or single table; reading tables into
data frames; generating vectors for image array axes; scaling and
writing images as 16-bit integers.  Known incompletenesses are
reading random group extensions, as well as
complex and array descriptor data types in binary tables.

This is the implementation in R+C of a fuzzy inference engine. It combines the speed of compiled C/C++ code, and the versatility of R in stadistics and graphical representation. suports several inference methods

This is a flexible implementation of the Kalman filter. It is entirely written in C and relies fully on linear algebra subroutines contained in BLAS and LAPACK. Due to the speed of the filter, the fitting of high-dimensional linear state space models to large datasets becomes possible. This package also contains a plot function for the visualization of the state vector and graphical diagnostics of the residuals.

This is a flexible implementation of the Kalman filter. It
is entirely written in C and relies fully on linear algebra
subroutines contained in BLAS and LAPACK. Due to the speed of
the filter, the fitting of high-dimensional linear state space
models to large datasets becomes possible. This package also
contains a plot function for the visualization of the state
vector and graphical diagnostics of the residuals.

David Luethi, Philipp Erb, Simon Otziger
This is a fast and flexible implementation of the Kalman
filter, which can deal with NAs. It is entirely written in C
and relies fully on linear algebra subroutines contained in
BLAS and LAPACK. Due to the speed of the filter, the fitting of
high-dimensional linear state space models to large datasets
becomes possible. This package also contains a plot function
for the visualization of the state vector and graphical
diagnostics of the residuals.