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/*
 *  Mathlib : A C Library of Special Functions
 *  Copyright (C) 1998 Ross Ihaka
 *  Copyright (C) 2000-2002 The R Development Core Team
 *  Copyright (C) 2003      The R Foundation
 *
 *  This program is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation; either version 2 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA.
 *
 *  DESCRIPTION
 *
 *  The "Student" t distribution quantile function.
 *
 *  NOTES
 *
 *  This is a C translation of the Fortran routine given in:
 *  Hill, G.W (1970) "Algorithm 396: Student's t-quantiles"
 *  CACM 13(10), 619-620.
 *
 *  ADDITIONS:
 *  - lower_tail, log_p
 *  - using  expm1() : takes care of  Lozy (1979) "Remark on Algo.", TOMS
 *  - Apply 2-term Taylor expansion as in
 *    Hill, G.W (1981) "Remark on Algo.396", ACM TOMS 7, 250-1
 *  - Improve the formula decision for 1 < df < 2
 */

#include "nmath.h"
#include "dpq.h"

double qt(double p, double ndf, int lower_tail, int log_p)
{
    const double eps = 1.e-12;

    double a, b, c, d, p_, P, q, x, y;
    Rboolean neg;

#ifdef IEEE_754
    if (ISNAN(p) || ISNAN(ndf))
    return p + ndf;
#endif
    if (p == R_DT_0) return ML_NEGINF;
    if (p == R_DT_1) return ML_POSINF;
    R_Q_P01_check(p);

    if (ndf < 1) /* FIXME:  not yet treated here */
    ML_ERR_return_NAN;

    /* FIXME: This test should depend on  ndf  AND p  !!
     * -----  and in fact should be replaced by
     * something like Abramowitz & Stegun 26.7.5 (p.949)
     */
    if (ndf > 1e20) return qnorm(p, 0., 1., lower_tail, log_p);

    p_ = R_D_qIv(p); /* note: exp(p) may underflow to 0; fix later */

    if((lower_tail && p_ > 0.5) || (!lower_tail && p_ < 0.5)) {
    neg = FALSE; P = 2 * R_D_Cval(p_);
    } else {
    neg = TRUE;  P = 2 * R_D_Lval(p_);
    } /* 0 <= P <= 1 ; P = 2*min(p_, 1 - p_)  in all cases */

    if (fabs(ndf - 2) < eps) {  /* df ~= 2 */
    if(P > 0)
        q = sqrt(2 / (P * (2 - P)) - 2);
    else { /* P = 0, but maybe = exp(p) ! */
        if(log_p) q = M_SQRT2 * exp(- .5 * R_D_Lval(p));
        else q = ML_POSINF;
    }
    }
    else if (ndf < 1 + eps) { /* df ~= 1  (df < 1 excluded above): Cauchy */
    if(P > 0)
        q = - tan((P+1) * M_PI_2);

    else { /* P = 0, but maybe p_ = exp(p) ! */
        if(log_p) q = M_1_PI * exp(-R_D_Lval(p));/* cot(e) ~ 1/e */
        else q = ML_POSINF;
    }
    }
    else {      /*-- usual case;  including, e.g.,  df = 1.1 */
    a = 1 / (ndf - 0.5);
    b = 48 / (a * a);
    c = ((20700 * a / b - 98) * a - 16) * a + 96.36;
    d = ((94.5 / (b + c) - 3) / b + 1) * sqrt(a * M_PI_2) * ndf;
    if(P > 0 || !log_p)
        y = pow(d * P, 2 / ndf);
    else /* P = 0 && log_p;  P = 2*exp(p) */
        y = exp(2 / ndf * (log(d) + M_LN2 + R_D_Lval(p)));

    if ((ndf < 2.1 && P > 0.5) || y > 0.05 + a) { /* P > P0(df) */
        /* Asymptotic inverse expansion about normal */
        if(P > 0 || !log_p)
        x = qnorm(0.5 * P, 0., 1., /*lower_tail*/TRUE, /*log_p*/FALSE);
        else /* P = 0 && log_p;  P = 2*exp(p') */
        x = qnorm( p,      0., 1., lower_tail,         /*log_p*/TRUE);

        y = x * x;
        if (ndf < 5)
        c += 0.3 * (ndf - 4.5) * (x + 0.6);
        c = (((0.05 * d * x - 5) * x - 7) * x - 2) * x + b + c;
        y = (((((0.4 * y + 6.3) * y + 36) * y + 94.5) / c
          - y - 3) / b + 1) * x;
        y = expm1(a * y * y);
    } else {
        y = ((1 / (((ndf + 6) / (ndf * y) - 0.089 * d - 0.822)
               * (ndf + 2) * 3) + 0.5 / (ndf + 4))
         * y - 1) * (ndf + 1) / (ndf + 2) + 1 / y;
    }
    q = sqrt(ndf * y);

    /* Now apply 2-term Taylor expansion improvement (1-term = Newton):
     * as by Hill (1981) [ref.above] */

    /* FIXME: This is can be far from optimal when log_p = TRUE !
     *    and probably also improvable when  lower_tail = FALSE */
    x = (pt(q, ndf, /*lower_tail = */FALSE, /*log_p = */FALSE) - P/2) /
        dt(q, ndf, /* give_log = */FALSE);
    /* Newton (=Taylor 1 term):
     *  q += x;
     * Taylor 2-term : */
    q += x * (1. + x * q * (ndf + 1) / (2 * (q * q + ndf)));
    }
    if(neg) q = -q;
    return q;
}