_a_v_a_s: _A_d_d_i_t_i_v_i_t_y _a_n_d _v_a_r_i_a_n_c_e _s_t_a_b_i_l_i_z_a_t_i_o_n _f_o_r _r_e_g_r_e_s_s_i_o_n
	
	     avas(x, y, wt, mon, lin, cat, circ, delrsq,yspan)
	
	_A_r_g_u_m_e_n_t_s:
	
	            x:
	     a matrix containing the independent variables.
	
	            y:
	     a vector containing the response variable.
	
	           wt:
	     an optional vector of weights.
	
	          mon:
	     an optional integer vector specifying which variables
	     are to be transformed by monotone transformations.
	     Positive values in mon refer to columns of the x matrix
	     and zero to the response variable.
	
	          lin:
	     an optional integer vector specifying which variables
	     are to be transformed by linear transformations.  Posi-
	     tive values in lin refer to columns of the x matrix and
	     zero to the response variable.
	
	          cat:
	     an optional integer vector specifying which variables
	     assume categorical values.  Positive values in cat
	     refer to columns of the x matrix and zero to the
	     response variable.
	
	         circ:
	     an integer vector specifying which variables assume
	     circular (periodic) values.  Positive values in circ
	     refer to columns of the x matrix and zero to the
	     response variable.
	
	       delrsq:
	     termination threshold.  Iteration stops when R-squared
	     changes by less than delrsq in 3 consecutive iterations
	     (default 0.01).
	
	        yspan:
	     Optional window size parameter for smoothing the vari-
	     ance. Range[0,1]. Default=0 (cross validated choice).
	     .5 is a reasonable alternative to try.
	
	     Value:
	
	     structure with the following components:
	
	       x:
	the input x matrix.
	
	       y:
	the input y vector.
	
	      tx:
	the transformed x values.
	
	      ty:
	the transformed y values.
	
	     rsq:
	the multiple R-squared value for the transformed values.
	
	       l:
	not used in this version of avas
	
	       m:
	not used in this version of avas
	
	   yspan:
	span used for smoothing the variance
	
	   iters:
	iteration number and rsq for that iteration
	
	  niters:
	number of iterations used
	
	     Rob Tibshirani, "Estimating optimal transformations for
	     regression". JASA Vol 83, No 402 Page 394 Version: Mar
	     18/1987
	
	_E_x_a_m_p_l_e_s:
	
	     TWOPI <- 8*atan(1)
	     x <- runif(200,0,TWOPI)
	     y <- exp(sin(x)+rnorm(200)/2)
	     a <- avas(x,y)
	     plot(a,ay)  # view the response transformation
	     plot(a,ax)  # view the carrier transformation
	     plot(ax,ay) # examine the linearity of the fitted model