Implementation of "Time-varying vector autoregressive models with stochastic volatility" by Kostas Triantafyllopoulos available at http://arxiv.org/abs/0802.0220 and published in Journal of Applied Statistics Vol. 38, No. 2 Feb 2011.

kf stands for Kalman Filter. This package can be seen as an advanced Kalman Filter and the name is short. 

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kf.cpp requires Boost uBLAS
kf_test.cpp and kf_speed.cpp require Boost Test and Boost Random 

Boost Installation on Ubuntu:
$ sudo apt-get install libboost-dev libboost-doc 

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Test with 
g++ kf_test.cpp; ./a.out

The correct output is
Running N test cases...

*** No errors detected


Test runtime with
g++ kf_speed.cpp; ./a.out

Running 1 test case...
gUnivariate runtime = 1
Bivariate runtime = 3
10-variate runtime = 47

*** No errors detected


Should compile with no warnings under -Wall.
Also runnable with g++ -O3 compiler optimization flag set.

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Please see test cases and code for usage.

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Note that there is one errata in the paper to mention:
In section 3.1, 
Pt = Rt − Kt K't / Qt
should be 
Pt = Rt − Kt K't * Qt

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Advanced C++ features: templates, default template arguments, operator overloading, overloaded operator overloading, default arguments, inline functions, multiple returns with reference parameters, macros

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Thanks to:
rgarcia