C
C     COMPUTE SUM VECTOR AND SUM-OF-PRODUCTS MATRIX:
      DO 105 JV = 1,IP
      TOTAL(JV) = 0.0D+00
      DO 105 JW = 1,IP
      SUMSQS(JV,JW) = 0.0D+00
  105 CONTINUE
      DO 110 JV = 1,IP
      DO 110 I = 1,N
      TOTAL(JV) = TOTAL(JV) + X(I,JV)
  110 CONTINUE
      DO 120 JV = 1,IP
      DO 120 JW = 1,IP
      DO 120 I = 1,N
      SUMSQS(JV,JW) = SUMSQS(JV,JW) + X(I,JV)*X(I,JW)
  120 CONTINUE
      DO 130 JV = 1,IP
      XMEAN(JV)  = TOTAL(JV)/XN
  130 CONTINUE
      DO 140 JV = 1,IP
      DO 140 JW = 1,IP
      SSDEVS(JV,JW) = SUMSQS(JV,JW)- TOTAL(JV)*TOTAL(JW)/XN
      VARHAT(JV,JW) = SSDEVS(JV,JW)/XN
  140 CONTINUE
C     WRITE SUMMARY STATISTICS FOR WHOLE SAMPLE:
      WRITE (6,10050)
      WRITE(6,21000)
      WRITE(6,22000)
      WRITE(6,10100) ( XMEAN(JV), JV = 1,IP )
      WRITE(6,10200)
      DO 150 JV = 1,IP
      WRITE(6,48000) ( VARHAT(JV,JW), JW=1,IP )
  150 CONTINUE
C
C
C     CALL SUBROUTINE TO COMPUTE DETERMINANT OF SAMPLE COVARIANCE
C     MATRIX:
C
      IDET = 1
      NRS1  =  0
      DO 2404 JV = 1,IP
      DO 2404 JW = 1,IP
      COVMX(JV,JW) = VARHAT(JV,JW)
 2404 CONTINUE
      CALL MATEQ(COVMX, IP,20,JFLG,DET,IDET,IV,NRS1,P,20)
C
C     GENERAL FORM OF CALL IS:
C     CALL MATEQ(A,M,N,JFLG,DET,IDET,IV,NRS1,P,LL)
C     SEE SUBROUTINE LISTING FOR FULLER EXPLANATION.
C     DET(VARHAT) = DET*10.0**IDET
C
      IF (JFLG .GT. 0) WRITE (6,60000) JFLG
C
C
      XIDET = IDET
C     XLGDET = DLOG(DET) + XIDET*ALOG(10.0)
      TRUDET = DET*(10.0**IDET)
      IF (JFLG .GT. 0) WRITE (6,62000) DET,IDET
C
C     MAX LIKELIHOOD =
C               (2*PI)**(-N*P/2)*(DET OF COV MX)**(-N/2)*EXP(-N*P/2)
C     COMPUTE LOG OF MAX LIKELIHOOD:
      XLL=-(XN*IP/2.0)*DLOG(2.0*PI)-(XN/2.0)*DLOG(TRUDET)-XN*IP/2.0
      XMN2LL = (-2.0)*XLL
      XM2LL(1) = XMN2LL
      WRITE(6,37000) XMN2LL
C       NO. OF PARAMETERS FOR UNCLUSTERED SAMPLE IS:
C     1 MEAN VECTOR + 1 COV MX.
      NOPARM = IP + IP*(IP+1)/2
      IPARM(1) = NOPARM
C
      AIC = XMN2LL + 2.0*NOPARM
      WRITE(6,70000) AIC
      SCH = XMN2LL + ALOG(XN)*NOPARM
      WRITE (6,71000) SCH
      AICVEC(1) = AIC
      SCHVEC(1) = SCH
      WRITE (6,10050)
C
C
C       PERFORM CLUSTERING FOR VARIOUS NUMBERS OF CLUSTERS, K:
C
      DO 850 K = 2,KUP
      WRITE (6,26000) K
C
C
C     SET CONSTANTS FOR THIS VALUE OF K.
C  PARAMETERS FOR MODEL WITH COMMON COVARIANCE MATRIX ARE
C  K MEAN VECTORS AND ONE COVARIANCE MATRIX AND K-1 INDEPENDENT
C  MIXING PROBABILITIES.
C
      NOPARM = K*IP + IP*(IP+1)/2 + (K-1)
C
C