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+/* Copyright 2005 Robert Kern (robert.kern@gmail.com)
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to
+ * the following conditions:
+ * 
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ * 
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef _RK_DISTR_
+#define _RK_DISTR_
+
+#include "randomkit.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* References:
+ * 
+ * Devroye, Luc. _Non-Uniform Random Variate Generation_.
+ *  Springer-Verlag, New York, 1986.
+ *  http://cgm.cs.mcgill.ca/~luc/rnbookindex.html
+ * 
+ * Kachitvichyanukul, V. and Schmeiser, B. W. Binomial Random Variate
+ *  Generation. Communications of the ACM, 31, 2 (February, 1988) 216.
+ *
+ * Hoermann, W. The Transformed Rejection Method for Generating Poisson Random
+ *  Variables. Insurance: Mathematics and Economics, (to appear)
+ *  http://citeseer.csail.mit.edu/151115.html
+ *
+ * Marsaglia, G. and Tsang, W. W. A Simple Method for Generating Gamma 
+ * Variables. ACM Transactions on Mathematical Software, Vol. 26, No. 3,
+ * September 2000, Pages 363–372.
+ */
+ 
+/* Normal distribution with mean=loc and standard deviation=scale. */
+extern double rk_normal(rk_state *state, double loc, double scale);
+
+/* Standard exponential distribution (mean=1) computed by inversion of the 
+ * CDF. */
+extern double rk_standard_exponential(rk_state *state);
+
+/* Exponential distribution with mean=scale. */
+extern double rk_exponential(rk_state *state, double scale);
+
+/* Uniform distribution on interval [loc, loc+scale). */
+extern double rk_uniform(rk_state *state, double loc, double scale);
+
+/* Standard gamma distribution with shape parameter. 
+ * When shape < 1, the algorithm given by (Devroye p. 304) is used.
+ * When shape == 1, a Exponential variate is generated.
+ * When shape > 1, the small and fast method of (Marsaglia and Tsang 2000) 
+ * is used.
+ */
+extern double rk_standard_gamma(rk_state *state, double shape);
+
+/* Gamma distribution with shape and scale. */
+extern double rk_gamma(rk_state *state, double shape, double scale);
+
+/* Beta distribution computed by combining two gamma variates (Devroye p. 432).
+ */
+extern double rk_beta(rk_state *state, double a, double b);
+
+/* Chi^2 distribution computed by transforming a gamma variate (it being a
+ * special case Gamma(df/2, 2)). */
+extern double rk_chisquare(rk_state *state, double df);
+
+/* Noncentral Chi^2 distribution computed by modifying a Chi^2 variate. */
+extern double rk_noncentral_chisquare(rk_state *state, double df, double nonc);
+
+/* F distribution computed by taking the ratio of two Chi^2 variates. */
+extern double rk_f(rk_state *state, double dfnum, double dfden);
+
+/* Noncentral F distribution computed by taking the ratio of a noncentral Chi^2
+ * and a Chi^2 variate. */
+extern double rk_noncentral_f(rk_state *state, double dfnum, double dfden, double nonc);
+
+/* Binomial distribution with n Bernoulli trials with success probability p.
+ * When n*p <= 30, the "Second waiting time method" given by (Devroye p. 525) is
+ * used. Otherwise, the BTPE algorithm of (Kachitvichyanukul and Schmeiser 1988)
+ * is used. */
+extern long rk_binomial(rk_state *state, long n, double p);
+
+/* Binomial distribution using BTPE. */
+extern long rk_binomial_btpe(rk_state *state, long n, double p);
+
+/* Binomial distribution using inversion and chop-down */
+extern long rk_binomial_inversion(rk_state *state, long n, double p);
+
+/* Negative binomial distribution computed by generating a Gamma(n, (1-p)/p)
+ * variate Y and returning a Poisson(Y) variate (Devroye p. 543). */
+extern long rk_negative_binomial(rk_state *state, long n, double p);
+
+/* Poisson distribution with mean=lam.
+ * When lam < 10, a basic algorithm using repeated multiplications of uniform
+ * variates is used (Devroye p. 504).
+ * When lam >= 10, algorithm PTRS from (Hoermann 1992) is used.
+ */
+extern long rk_poisson(rk_state *state, double lam);
+
+/* Poisson distribution computed by repeated multiplication of uniform variates.
+ */
+extern long rk_poisson_mult(rk_state *state, double lam);
+
+/* Poisson distribution computer by the PTRS algorithm. */
+extern long rk_poisson_ptrs(rk_state *state, double lam);
+
+/* Standard Cauchy distribution computed by dividing standard gaussians 
+ * (Devroye p. 451). */
+extern double rk_standard_cauchy(rk_state *state);
+
+/* Standard t-distribution with df degrees of freedom (Devroye p. 445 as
+ * corrected in the Errata). */
+extern double rk_standard_t(rk_state *state, double df);
+
+/* von Mises circular distribution with center mu and shape kappa on [-pi,pi]
+ * (Devroye p. 476 as corrected in the Errata). */
+extern double rk_vonmises(rk_state *state, double mu, double kappa);
+
+/* Pareto distribution via inversion (Devroye p. 262) */
+extern double rk_pareto(rk_state *state, double a);
+
+/* Weibull distribution via inversion (Devroye p. 262) */
+extern double rk_weibull(rk_state *state, double a);
+
+/* Power distribution via inversion (Devroye p. 262) */
+extern double rk_power(rk_state *state, double a);
+
+/* Laplace distribution */
+extern double rk_laplace(rk_state *state, double loc, double scale);
+
+/* Gumbel distribution */
+extern double rk_gumbel(rk_state *state, double loc, double scale);
+
+/* Logistic distribution */
+extern double rk_logistic(rk_state *state, double loc, double scale);
+
+/* Log-normal distribution */
+extern double rk_lognormal(rk_state *state, double mean, double sigma);
+
+/* Rayleigh distribution */
+extern double rk_rayleigh(rk_state *state, double mode);
+
+/* Wald distribution */
+extern double rk_wald(rk_state *state, double mean, double scale);
+
+/* Zipf distribution */
+extern long rk_zipf(rk_state *state, double a);
+
+/* Geometric distribution */
+extern long rk_geometric(rk_state *state, double p);
+extern long rk_geometric_search(rk_state *state, double p);
+extern long rk_geometric_inversion(rk_state *state, double p);
+
+/* Hypergeometric distribution */
+extern long rk_hypergeometric(rk_state *state, long good, long bad, long sample);
+extern long rk_hypergeometric_hyp(rk_state *state, long good, long bad, long sample);
+extern long rk_hypergeometric_hrua(rk_state *state, long good, long bad, long sample);
+
+/* Triangular distribution */
+extern double rk_triangular(rk_state *state, double left, double mode, double right);
+
+/* Logarithmic series distribution */
+extern long rk_logseries(rk_state *state, double p);
+
+#ifdef __cplusplus
+}
+#endif
+
+
+#endif /* _RK_DISTR_ */