@@ -100,8 +100,8 @@ generators, 'in addition' to the standard PRNG in NumPy. The included PRNGs are
100100 arbitrary number of steps or generating independent streams. See the
101101 `Random123 `_ page for more details about this class of PRNG.
102102* Other cryptographic-based generators: :class: `~randomgen.aes.AESCounter `,
103- :class: `~randomgen.speck128.SPECK128 `, :class: `~randomgen.chacha.ChaCha `, and
104- :class: `~randomgen.hc128.HC128 `.
103+ :class: `~randomgen.speck128.SPECK128 `, :class: `~randomgen.chacha.ChaCha `,
104+ :class: `~randomgen.hc128.HC128 `, and :class: ` ~randomgen.blabla.BlaBla ` .
105105* XoroShiro128+/++ - Improved version of XorShift128+ with better performance
106106 and statistical quality. Like the XorShift generators, it can be jumped
107107 to produce multiple streams in parallel applications. See
@@ -114,6 +114,8 @@ generators, 'in addition' to the standard PRNG in NumPy. The included PRNGs are
114114 :meth: `~randomgen.xorshift1024.Xorshift1024.jumped ` for details. More information
115115 about these PRNGs is available at the
116116 `xorshift, xoroshiro and xoshiro authors' page `_.
117+ * Other PRNGs including :class: `~randomgen.romu.Romu `, :class: `~randomgen.tyche.Tyche `,
118+ and :class: `~randomgen.squares.Squares `.
117119
118120.. _`NumPy's documentation` : https://docs.scipy.org/doc/numpy/reference/routines.random.html
119121.. _`dSFMT authors' page` : http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/
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