Fix lint errors

rajutkarsh07 · Dec 30, 2017 · d494b8c · d494b8c
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diff --git a/lib/node_modules/@stdlib/random/base/improved-ziggurat/lib/sample_tail.js b/lib/node_modules/@stdlib/random/base/improved-ziggurat/lib/sample_tail.js
@@ -10,9 +10,9 @@ var ln = require( '@stdlib/math/base/special/ln' );
 /**
 * Transforms the tail of the normal distribution to the unit interval and then uses rejection technique to generate standard normal variable.
 *
-* #### References
+* ## References
 *
-* * Marsaglia, George. 1964. "Generating a Variable from the Tail of the Normal Distribution." *Technometrics* 6 (1): 101–2. doi:[10.1080/00401706.1964.10490150](http://dx.doi.org/10.1080/00401706.1964.10490150).
+* -   Marsaglia, George. 1964. "Generating a Variable from the Tail of the Normal Distribution." _Technometrics_ 6 (1): 101–2. doi:[10.1080/00401706.1964.10490150](http://dx.doi.org/10.1080/00401706.1964.10490150).
 *
 * @private
 * @param {Function} rand - random number generator

diff --git a/lib/node_modules/@stdlib/random/base/invgamma/lib/invgamma.js b/lib/node_modules/@stdlib/random/base/invgamma/lib/invgamma.js
@@ -10,17 +10,17 @@ var factory = require( './factory.js' );
 /**
 * Returns a random number drawn from an inverse gamma distribution.
 *
-* #### Method
+* ## Method
 *
 * When
 *
-* ```
+* ``` tex
 * X \sim \operatorname{Gamma}( \alpha, \beta )
 * ```
 *
 * then
 *
-* ```
+* ``` tex
 * \frac{1}{X} \sim \operatorname{InvGamma}\left( \alpha, \tfrac{1}{beta} \right)
 * ```
 *

diff --git a/lib/node_modules/@stdlib/random/base/minstd-shuffle/lib/minstd_shuffled.js b/lib/node_modules/@stdlib/random/base/minstd-shuffle/lib/minstd_shuffled.js
@@ -11,16 +11,16 @@ var randint32 = require( './rand_int32.js' );
 /**
 * Generates a pseudorandom integer on the interval \\( [1,2^{31}-1) \\).
 *
-* #### Method
+* ## Method
 *
 * This implementation shuffles the output of a linear congruential pseudorandom number generator (LCG) using a shuffle table in accordance with the Bays-Durham algorithm.
 *
 *
-* #### References
+* ## References
 *
-* * Bays, Carter, and S. D. Durham. 1976. "Improving a Poor Random Number Generator." *ACM Transactions on Mathematical Software* 2 (1). New York, NY, USA: ACM: 59–64. doi:[10.1145/355666.355670](http://dx.doi.org/10.1145/355666.355670).
-* * Herzog, T.N., and G. Lord. 2002. *Applications of Monte Carlo Methods to Finance and Insurance*. ACTEX Publications. [https://books.google.com/books?id=vC7I\\\_gdX-A0C](https://books.google.com/books?id=vC7I\_gdX-A0C).
-* * Press, William H., Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. 1992. *Numerical Recipes in C: The Art of Scientific Computing, Second Edition*. Cambridge University Press.
+* -   Bays, Carter, and S. D. Durham. 1976. "Improving a Poor Random Number Generator." _ACM Transactions on Mathematical Software_ 2 (1). New York, NY, USA: ACM: 59–64. doi:[10.1145/355666.355670](http://dx.doi.org/10.1145/355666.355670).
+* -   Herzog, T.N., and G. Lord. 2002. _Applications of Monte Carlo Methods to Finance and Insurance_. ACTEX Publications. [https://books.google.com/books?id=vC7I\\\_gdX-A0C](https://books.google.com/books?id=vC7I\_gdX-A0C).
+* -   Press, William H., Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. 1992. _Numerical Recipes in C: The Art of Scientific Computing, Second Edition_. Cambridge University Press.
 *
 *
 * @function minstd

diff --git a/lib/node_modules/@stdlib/random/base/minstd/lib/minstd.js b/lib/node_modules/@stdlib/random/base/minstd/lib/minstd.js
@@ -11,7 +11,7 @@ var randint32 = require( './rand_int32.js' );
 /**
 * Generates a pseudorandom integer on the interval \\( [1,2^{31}-1) \\).
 *
-* #### Method
+* ## Method
 *
 * Linear congruential generators (LCGs) use the recurrence relation
 *
@@ -55,18 +55,18 @@ var randint32 = require( './rand_int32.js' );
 * 16807 \cdot (m - 1) \approx 2^{46}
 * ```
 *
-* The values for \\( a \\), \\( c \\), and \\( m \\) are taken from Park and Miller, "Random Number Generators: Good Ones Are Hard To Find". Park's and Miller's article is also the basis for a recipe in the second edition of *Numerical Recipes in C*.
+* The values for \\( a \\), \\( c \\), and \\( m \\) are taken from Park and Miller, "Random Number Generators: Good Ones Are Hard To Find". Park's and Miller's article is also the basis for a recipe in the second edition of _Numerical Recipes in C_.
 *
 *
-* #### Notes
+* ## Notes
 *
-* * The generator has a period of approximately \\(2.1\mbox{e}9\\) (see [Numerical Recipes in C, 2nd Edition](#references), p. 279).
+* -   The generator has a period of approximately \\(2.1\mbox{e}9\\) (see [Numerical Recipes in C, 2nd Edition](#references), p. 279).
 *
 *
-* #### References
+* ## References
 *
-* * Park, S. K., and K. W. Miller. 1988. "Random Number Generators: Good Ones Are Hard to Find." *Communications of the ACM* 31 (10). New York, NY, USA: ACM: 1192–1201. doi:[10.1145/63039.63042](http://dx.doi.org/10.1145/63039.63042).
-* * Press, William H., Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. 1992. *Numerical Recipes in C: The Art of Scientific Computing, Second Edition*. Cambridge University Press.
+* -   Park, S. K., and K. W. Miller. 1988. "Random Number Generators: Good Ones Are Hard to Find." _Communications of the ACM_ 31 (10). New York, NY, USA: ACM: 1192–1201. doi:[10.1145/63039.63042](http://dx.doi.org/10.1145/63039.63042).
+* -   Press, William H., Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. 1992. _Numerical Recipes in C: The Art of Scientific Computing, Second Edition_. Cambridge University Press.
 *
 *
 * @function minstd

diff --git a/lib/node_modules/@stdlib/random/base/poisson/lib/knuth.js b/lib/node_modules/@stdlib/random/base/poisson/lib/knuth.js
@@ -10,14 +10,14 @@ var exp = require( '@stdlib/math/base/special/exp' );
 /**
 * Returns a pseudorandom number drawn from a Poisson distribution.
 *
-* #### Notes
+* ## Notes
 *
-* * Appropriate for \\(lambda < 30\\).
+* -   Appropriate for \\(lambda < 30\\).
 *
 *
-* #### References
+* ## References
 *
-* * Knuth, Donald E. 1997. *The Art of Computer Programming, Volume 2 (3rd Ed.): Seminumerical Algorithms*. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
+* -   Knuth, Donald E. 1997. _The Art of Computer Programming, Volume 2 (3rd Ed.): Seminumerical Algorithms_. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
 *
 *
 * @private

diff --git a/lib/node_modules/@stdlib/random/base/poisson/lib/poisson.js b/lib/node_modules/@stdlib/random/base/poisson/lib/poisson.js
@@ -10,15 +10,15 @@ var factory = require( './factory.js' );
 /**
 * Returns a pseudorandom number drawn from a Poisson distribution with parameter `lambda`.
 *
-* #### Method
+* ## Method
 *
-* * When \\(\lambda < 30\\), use Knuth's method.
-* * When \\(lambda \geq 30\\), use transformed rejection method as Knuth's method does not scale well with \\(\lambda\\).
+* -   When \\(\lambda < 30\\), use Knuth's method.
+* -   When \\(lambda \geq 30\\), use transformed rejection method as Knuth's method does not scale well with \\(\lambda\\).
 *
-* #### References
+* ## References
 *
-* * Knuth, Donald E. 1997. *The Art of Computer Programming, Volume 2 (3rd Ed.): Seminumerical Algorithms*. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
-* * Hörmann, W. 1993. "The transformed rejection method for generating Poisson random variables." *Insurance: Mathematics and Economics* 12 (1): 39–45. doi:[10.1016/0167-6687(93)90997-4][@hormann:1993b].
+* -   Knuth, Donald E. 1997. _The Art of Computer Programming, Volume 2 (3rd Ed.): Seminumerical Algorithms_. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
+* -   Hörmann, W. 1993. "The transformed rejection method for generating Poisson random variables." _Insurance: Mathematics and Economics_ 12 (1): 39–45. doi:[10.1016/0167-6687(93)90997-4][@hormann:1993b].
 *
 * [@hormann:1993b]: http://dx.doi.org/10.1016/0167-6687(93)90997-4
 *

diff --git a/lib/node_modules/@stdlib/random/base/poisson/lib/rejection.js b/lib/node_modules/@stdlib/random/base/poisson/lib/rejection.js
@@ -22,9 +22,9 @@ var ONE_360 = 1.0 / 360.0;
 /**
 * Returns a pseudorandom number drawn from a Poisson distribution with parameter `lambda`.
 *
-* #### References
+* ## References
 *
-* * Hörmann, W. 1993. "The transformed rejection method for generating Poisson random variables." *Insurance: Mathematics and Economics* 12 (1): 39–45. doi:[10.1016/0167-6687(93)90997-4][@hormann:1993b].
+* -   Hörmann, W. 1993. "The transformed rejection method for generating Poisson random variables." _Insurance: Mathematics and Economics_ 12 (1): 39–45. doi:[10.1016/0167-6687(93)90997-4][@hormann:1993b].
 *
 * [@hormann:1993b]: http://dx.doi.org/10.1016/0167-6687(93)90997-4
 *