_N_o_n_p_a_r_a_m_e_t_r_i_c _A_B_C _c_o_n_f_i_d_e_n_c_e _l_i_m_i_t_s
	
	     abcnon(x, tt, epsilon=0.001,
	      alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975))
	
	_A_r_g_u_m_e_n_t_s:
	
	           x : the data. Must be either a vector, or a
	               matrix whose rows are the observations
	
	          tt : function defining the parameter in the resam-
	               pling form tt(p,x), where p is the vector of
	               proportions and x is the data
	
	     epsilon : optional argument specifying step size for
	               finite difference calculations
	
	       alpha : optional argument specifying confidence lev-
	               els desired
	
	_V_a_l_u_e_s:
	
	     list with following components
	
	 limits : The estimated confidence points, from the ABC and
	          standard normal methods
	
	  stats : list consisting of t0=observed value of tt,
	          sighat=infinitesimal jackknife estimate
	           of standard error of tt, bhat= estimated bias
	
	constants : list consisting of a=acceleration constant,
	          z0=bias adjustment, cq=curvature component
	
	 tt.inf : approximate influence components of tt
	
	     pp : matrix whose rows are the resampling points in the
	          least favourable family .  The abc confidence
	          points are the function tt evaluated at these
	          points
	
	_R_e_f_e_r_e_n_c_e_s:
	
	     Efron, B, and DiCiccio, T. (1992) More accurate confi-
	     dence intervals in exponential families. Biometrika 79,
	     pages 231-245.
	
	     Efron, B. and Tibshirani, R. (1993) An Introduction to
	     the Bootstrap.  Chapman and Hall, New York, London.