title: Regular expression to extract one string without prefix or suffix strings in R-tidyverse-stringr
body: <p>I have a list of strings which has four components:</p>
  
  <p><code>a_b_c_d</code> where:</p>
  
  <ul>
  <li><code>a</code> has 3 patterns of strings: <code>str</code>, <code>jtp</code> and <code>mdl</code></li>
  <li><code>b</code> has 5 patterns of strings: <code>HBW</code>, <code>HBS</code>,<code>HBO</code>,<code>NHBB</code> and <code>NHBO</code></li>
  <li><code>c</code> has 4 patterns of string: <code>L</code>, <code>M</code>, <code>H</code> and <code>ALL</code></li>
  <li><code>d</code> has 4 patterns of strings: <code>NMT</code>, <code>MC</code>, <code>CAR</code> and <code>PT</code></li>
  <li><code>a</code>, <code>b</code>, <code>c</code> and <code>d</code> are connected each other by <code>_</code> in order</li>
  <li>Some records do not have <code>b</code>, <code>c</code> and <code>d</code></li>
  </ul>
  
  <p>I need to extract <code>c</code> from the lists as a new field <code>income</code>. If <code>c</code> does not exist, it should be replaced by <code>NA</code>. Following is actual data frame that I use:</p>
  
  <pre><code>df &lt;- c(
  "str_HBW_L_NMT" ,"str_HBW_M_NMT" ,"str_HBW_H_NMT" ,"str_HBW_L_MC" ,"str_HBW_M_MC" ,"str_HBW_H_MC" ,
  "str_HBW_L_CAR" ,"str_HBW_M_CAR" ,"str_HBW_H_CAR" ,"str_HBW_L_PT" ,"str_HBW_M_PT" ,"str_HBW_H_PT" ,
  "str_HBS_L_NMT" ,"str_HBS_M_NMT" ,"str_HBS_H_NMT" ,"str_HBS_L_MC" ,"str_HBS_M_MC" ,"str_HBS_H_MC" ,
  "str_HBS_L_CAR" ,"str_HBS_M_CAR" ,"str_HBS_H_CAR" ,"str_HBS_L_PT" ,"str_HBS_M_PT" ,"str_HBS_H_PT" ,
  "str_HBO_L_NMT" ,"str_HBO_M_NMT" ,"str_HBO_H_NMT" ,"str_HBO_L_MC" ,"str_HBO_M_MC" ,"str_HBO_H_MC" ,
  "str_HBO_L_CAR" ,"str_HBO_M_CAR" ,"str_HBO_H_CAR" ,"str_HBO_L_PT" ,"str_HBO_M_PT" ,"str_HBO_H_PT" ,
  "str_NHBB_L_NMT","str_NHBB_M_NMT","str_NHBB_H_NMT","str_NHBB_L_MC","str_NHBB_M_MC","str_NHBB_H_MC",
  "str_NHBB_L_CAR","str_NHBB_M_CAR","str_NHBB_H_CAR","str_NHBB_L_PT","str_NHBB_M_PT","str_NHBB_H_PT",
  "str_NHBO_L_NMT","str_NHBO_M_NMT","str_NHBO_H_NMT","str_NHBO_L_MC","str_NHBO_M_MC","str_NHBO_H_MC",
  "str_NHBO_L_CAR","str_NHBO_M_CAR","str_NHBO_H_CAR","str_NHBO_L_PT","str_NHBO_M_PT","str_NHBO_H_PT",
  "str_HBW_L"     ,"str_HBW_M"     ,"str_HBW_H"     ,"str_HBS_L"    ,"str_HBS_M"    ,"str_HBS_H"    ,
  "str_HBO_L"     ,"str_HBO_M"     ,"str_HBO_H"     ,"str_NHBB_L"   ,"str_NHBB_M"   ,"str_NHBB_H"   ,
  "str_NHBO_L"    ,"str_NHBO_M"    ,"str_NHBO_H"    ,"str_HBW"      ,"str_HBS"      ,"str_HBO"      ,
  "str_NHBB"      ,"str_NHBO"      ,"str_L"         ,"str_M"        ,"str_H"        ,"str_ALL"      ,
  "jtp_HBW_L_NMT" ,"jtp_HBW_M_NMT" ,"jtp_HBW_H_NMT" ,"jtp_HBW_L_MC" ,"jtp_HBW_M_MC" ,"jtp_HBW_H_MC" ,
  "jtp_HBW_L_CAR" ,"jtp_HBW_M_CAR" ,"jtp_HBW_H_CAR" ,"jtp_HBW_L_PT" ,"jtp_HBW_M_PT" ,"jtp_HBW_H_PT" ,
  "jtp_HBS_L_NMT" ,"jtp_HBS_M_NMT" ,"jtp_HBS_H_NMT" ,"jtp_HBS_L_MC" ,"jtp_HBS_M_MC" ,"jtp_HBS_H_MC" ,
  "jtp_HBS_L_CAR" ,"jtp_HBS_M_CAR" ,"jtp_HBS_H_CAR" ,"jtp_HBS_L_PT" ,"jtp_HBS_M_PT" ,"jtp_HBS_H_PT" ,
  "jtp_HBW_L"     ,"jtp_HBW_M"     ,"jtp_HBW_H"     ,"jtp_HBS_L"    ,"jtp_HBS_M"    ,"jtp_HBS_H"    ,
  "jtp_HBW"       ,"jtp_HBS"       ,"jtp_L"         ,"jtp_M"        ,"jtp_H"        ,"jtp_ALL"      ,
  "mdl_HBW_L_NMT" ,"mdl_HBW_M_NMT" ,"mdl_HBW_H_NMT" ,"mdl_HBW_L_MC" ,"mdl_HBW_M_MC" ,"mdl_HBW_H_MC" ,
  "mdl_HBW_L_CAR" ,"mdl_HBW_M_CAR" ,"mdl_HBW_H_CAR" ,"mdl_HBW_L_PT" ,"mdl_HBW_M_PT" ,"mdl_HBW_H_PT" ,
  "mdl_HBS_L_NMT" ,"mdl_HBS_M_NMT" ,"mdl_HBS_H_NMT" ,"mdl_HBS_L_MC" ,"mdl_HBS_M_MC" ,"mdl_HBS_H_MC" ,
  "mdl_HBS_L_CAR" ,"mdl_HBS_M_CAR" ,"mdl_HBS_H_CAR" ,"mdl_HBS_L_PT" ,"mdl_HBS_M_PT" ,"mdl_HBS_H_PT" ,
  "mdl_HBO_L_NMT" ,"mdl_HBO_M_NMT" ,"mdl_HBO_H_NMT" ,"mdl_HBO_L_MC" ,"mdl_HBO_M_MC" ,"mdl_HBO_H_MC" ,
  "mdl_HBO_L_CAR" ,"mdl_HBO_M_CAR" ,"mdl_HBO_H_CAR" ,"mdl_HBO_L_PT" ,"mdl_HBO_M_PT" ,"mdl_HBO_H_PT" ,
  "mdl_NHBB_L_NMT","mdl_NHBB_M_NMT","mdl_NHBB_H_NMT","mdl_NHBB_L_MC","mdl_NHBB_M_MC","mdl_NHBB_H_MC",
  "mdl_NHBB_L_CAR","mdl_NHBB_M_CAR","mdl_NHBB_H_CAR","mdl_NHBB_L_PT","mdl_NHBB_M_PT","mdl_NHBB_H_PT",
  "mdl_NHBO_L_NMT","mdl_NHBO_M_NMT","mdl_NHBO_H_NMT","mdl_NHBO_L_MC","mdl_NHBO_M_MC","mdl_NHBO_H_MC",
  "mdl_NHBO_L_CAR","mdl_NHBO_M_CAR","mdl_NHBO_H_CAR","mdl_NHBO_L_PT","mdl_NHBO_M_PT","mdl_NHBO_H_PT",
  "mdl_HBW_L"     ,"mdl_HBW_M"     ,"mdl_HBW_H"     ,"mdl_HBS_L"    ,"mdl_HBS_M"    ,"mdl_HBS_H"    ,
  "mdl_HBO_L"     ,"mdl_HBO_M"     ,"mdl_HBO_H"     ,"mdl_NHBB_L"   ,"mdl_NHBB_M"   ,"mdl_NHBB_H"   ,
  "mdl_NHBO_L"    ,"mdl_NHBO_M"    ,"mdl_NHBO_H"    ,"mdl_HBW"      ,"mdl_HBS"      ,"mdl_HBO"      ,
  "mdl_NHBB"      ,"mdl_NHBO"      ,"mdl_L"         ,"mdl_M"        ,"mdl_H"        ,"mdl_ALL"
  )
  </code></pre>
  
  <p>I did lots of trials but could not extract it correctly. Following is an example of my script:</p>
  
  <pre><code>df %&gt;% mutate(income=str_extract_all(string=name,
          pattern="(?!str|jtp|mdl|HBW|HBS|HBO|NHBB|NHBO|_)[L|M|H|(ALL)](?!NMT|MC|CAR|PT|_)"))
  </code></pre>
  
  <p>Do you have any suggestion to extract desired output as show below?
  I prefer to use <code>tidyverse</code> and <code>stringr</code> but <code>base</code> function is also applicable instead of <code>stringr</code>. </p>
  
  <pre><code>      name          income
  1    str_HBW_L_NMT  L
  2    str_HBW_M_NMT  M
  3    str_HBW_H_NMT  H
  4     str_HBW_L_MC  L
  5     str_HBW_M_MC  M
  6     str_HBW_H_MC  H
  7    str_HBW_L_CAR  L
  8    str_HBW_M_CAR  M
  9    str_HBW_H_CAR  H
  10    str_HBW_L_PT  L
  11    str_HBW_M_PT  M
  12    str_HBW_H_PT  H
  13   str_HBS_L_NMT  L
  14   str_HBS_M_NMT  M
  15   str_HBS_H_NMT  H
  16    str_HBS_L_MC  L
  17    str_HBS_M_MC  M
  18    str_HBS_H_MC  H
  19   str_HBS_L_CAR  L
  20   str_HBS_M_CAR  M
  21   str_HBS_H_CAR  H
  22    str_HBS_L_PT  L
  23    str_HBS_M_PT  M
  24    str_HBS_H_PT  H
  25   str_HBO_L_NMT  L
  26   str_HBO_M_NMT  M
  27   str_HBO_H_NMT  H
  28    str_HBO_L_MC  L
  29    str_HBO_M_MC  M
  30    str_HBO_H_MC  H
  31   str_HBO_L_CAR  L
  32   str_HBO_M_CAR  M
  33   str_HBO_H_CAR  H
  34    str_HBO_L_PT  L
  35    str_HBO_M_PT  M
  36    str_HBO_H_PT  H
  37  str_NHBB_L_NMT  L
  38  str_NHBB_M_NMT  M
  39  str_NHBB_H_NMT  H
  40   str_NHBB_L_MC  L
  41   str_NHBB_M_MC  M
  42   str_NHBB_H_MC  H
  43  str_NHBB_L_CAR  L
  44  str_NHBB_M_CAR  M
  45  str_NHBB_H_CAR  H
  46   str_NHBB_L_PT  L
  47   str_NHBB_M_PT  M
  48   str_NHBB_H_PT  H
  49  str_NHBO_L_NMT  L
  50  str_NHBO_M_NMT  M
  51  str_NHBO_H_NMT  H
  52   str_NHBO_L_MC  L
  53   str_NHBO_M_MC  M
  54   str_NHBO_H_MC  H
  55  str_NHBO_L_CAR  L
  56  str_NHBO_M_CAR  M
  57  str_NHBO_H_CAR  H
  58   str_NHBO_L_PT  L
  59   str_NHBO_M_PT  M
  60   str_NHBO_H_PT  H
  61       str_HBW_L  L
  62       str_HBW_M  M
  63       str_HBW_H  H
  64       str_HBS_L  L
  65       str_HBS_M  M
  66       str_HBS_H  H
  67       str_HBO_L  L
  68       str_HBO_M  M
  69       str_HBO_H  H
  70      str_NHBB_L  L
  71      str_NHBB_M  M
  72      str_NHBB_H  H
  73      str_NHBO_L  L
  74      str_NHBO_M  M
  75      str_NHBO_H  H
  76         str_HBW  &lt;N/A&gt;
  77         str_HBS  &lt;N/A&gt;
  78         str_HBO  &lt;N/A&gt;
  79        str_NHBB  &lt;N/A&gt;
  80        str_NHBO  &lt;N/A&gt;
  81           str_L  L
  82           str_M  M
  83           str_H  H
  84         str_ALL  ALL
  85   jtp_HBW_L_NMT  L
  86   jtp_HBW_M_NMT  M
  87   jtp_HBW_H_NMT  H
  88    jtp_HBW_L_MC  L
  89    jtp_HBW_M_MC  M
  90    jtp_HBW_H_MC  H
  91   jtp_HBW_L_CAR  L
  92   jtp_HBW_M_CAR  M
  93   jtp_HBW_H_CAR  H
  94    jtp_HBW_L_PT  L
  95    jtp_HBW_M_PT  M
  96    jtp_HBW_H_PT  H
  97   jtp_HBS_L_NMT  L
  98   jtp_HBS_M_NMT  M
  99   jtp_HBS_H_NMT  H
  100   jtp_HBS_L_MC  L
  101   jtp_HBS_M_MC  M
  102   jtp_HBS_H_MC  H
  103  jtp_HBS_L_CAR  L
  104  jtp_HBS_M_CAR  M
  105  jtp_HBS_H_CAR  H
  106   jtp_HBS_L_PT  L
  107   jtp_HBS_M_PT  M
  108   jtp_HBS_H_PT  H
  109      jtp_HBW_L  L
  110      jtp_HBW_M  M
  111      jtp_HBW_H  H
  112      jtp_HBS_L  L
  113      jtp_HBS_M  M
  114      jtp_HBS_H  H
  115        jtp_HBW  &lt;N/A&gt;
  116        jtp_HBS  &lt;N/A&gt;
  117          jtp_L  L
  118          jtp_M  M
  119          jtp_H  H
  120        jtp_ALL  ALL
  121  mdl_HBW_L_NMT  L
  122  mdl_HBW_M_NMT  M
  123  mdl_HBW_H_NMT  H
  124   mdl_HBW_L_MC  L
  125   mdl_HBW_M_MC  M
  126   mdl_HBW_H_MC  H
  127  mdl_HBW_L_CAR  L
  128  mdl_HBW_M_CAR  M
  129  mdl_HBW_H_CAR  H
  130   mdl_HBW_L_PT  L
  131   mdl_HBW_M_PT  M
  132   mdl_HBW_H_PT  H
  133  mdl_HBS_L_NMT  L
  134  mdl_HBS_M_NMT  M
  135  mdl_HBS_H_NMT  H
  136   mdl_HBS_L_MC  L
  137   mdl_HBS_M_MC  M
  138   mdl_HBS_H_MC  H
  139  mdl_HBS_L_CAR  L
  140  mdl_HBS_M_CAR  M
  141  mdl_HBS_H_CAR  H
  142   mdl_HBS_L_PT  L
  143   mdl_HBS_M_PT  M
  144   mdl_HBS_H_PT  H
  145  mdl_HBO_L_NMT  L
  146  mdl_HBO_M_NMT  M
  147  mdl_HBO_H_NMT  H
  148   mdl_HBO_L_MC  L
  149   mdl_HBO_M_MC  M
  150   mdl_HBO_H_MC  H
  151  mdl_HBO_L_CAR  L
  152  mdl_HBO_M_CAR  M
  153  mdl_HBO_H_CAR  H
  154   mdl_HBO_L_PT  L
  155   mdl_HBO_M_PT  M
  156   mdl_HBO_H_PT  H
  157 mdl_NHBB_L_NMT  L
  158 mdl_NHBB_M_NMT  M
  159 mdl_NHBB_H_NMT  H
  160  mdl_NHBB_L_MC  L
  161  mdl_NHBB_M_MC  M
  162  mdl_NHBB_H_MC  H
  163 mdl_NHBB_L_CAR  L
  164 mdl_NHBB_M_CAR  M
  165 mdl_NHBB_H_CAR  H
  166  mdl_NHBB_L_PT  L
  167  mdl_NHBB_M_PT  M
  168  mdl_NHBB_H_PT  H
  169 mdl_NHBO_L_NMT  L
  170 mdl_NHBO_M_NMT  M
  171 mdl_NHBO_H_NMT  H
  172  mdl_NHBO_L_MC  L
  173  mdl_NHBO_M_MC  M
  174  mdl_NHBO_H_MC  H
  175 mdl_NHBO_L_CAR  L
  176 mdl_NHBO_M_CAR  M
  177 mdl_NHBO_H_CAR  H
  178  mdl_NHBO_L_PT  L
  179  mdl_NHBO_M_PT  M
  180  mdl_NHBO_H_PT  H
  181      mdl_HBW_L  L
  182      mdl_HBW_M  M
  183      mdl_HBW_H  H
  184      mdl_HBS_L  L
  185      mdl_HBS_M  M
  186      mdl_HBS_H  H
  187      mdl_HBO_L  L
  188      mdl_HBO_M  M
  189      mdl_HBO_H  H
  190     mdl_NHBB_L  L
  191     mdl_NHBB_M  M
  192     mdl_NHBB_H  H
  193     mdl_NHBO_L  L
  194     mdl_NHBO_M  M
  195     mdl_NHBO_H  H
  196        mdl_HBW  &lt;N/A&gt;
  197        mdl_HBS  &lt;N/A&gt;
  198        mdl_HBO  &lt;N/A&gt;
  199       mdl_NHBB  &lt;N/A&gt;
  200       mdl_NHBO  &lt;N/A&gt;
  201          mdl_L  L
  202          mdl_M  M
  203          mdl_H  H
  204        mdl_ALL  ALL
  </code></pre>
  
  <p>============== NEW EXAMPLE OF DATA FRAME ==========
  Records containing only <code>c</code> or <code>d</code> were added at the top of original df.</p>
  
  <pre><code>df &lt;- c(
  "NMT","MC","CAR","PT","L","M","H","ALL",
  "str_HBW_L_NMT" ,"str_HBW_M_NMT" ,"str_HBW_H_NMT" ,"str_HBW_L_MC" ,"str_HBW_M_MC" ,"str_HBW_H_MC" ,
  "str_HBW_L_CAR" ,"str_HBW_M_CAR" ,"str_HBW_H_CAR" ,"str_HBW_L_PT" ,"str_HBW_M_PT" ,"str_HBW_H_PT" ,
  "str_HBS_L_NMT" ,"str_HBS_M_NMT" ,"str_HBS_H_NMT" ,"str_HBS_L_MC" ,"str_HBS_M_MC" ,"str_HBS_H_MC" ,
  "str_HBS_L_CAR" ,"str_HBS_M_CAR" ,"str_HBS_H_CAR" ,"str_HBS_L_PT" ,"str_HBS_M_PT" ,"str_HBS_H_PT" ,
  "str_HBO_L_NMT" ,"str_HBO_M_NMT" ,"str_HBO_H_NMT" ,"str_HBO_L_MC" ,"str_HBO_M_MC" ,"str_HBO_H_MC" ,
  "str_HBO_L_CAR" ,"str_HBO_M_CAR" ,"str_HBO_H_CAR" ,"str_HBO_L_PT" ,"str_HBO_M_PT" ,"str_HBO_H_PT" ,
  "str_NHBB_L_NMT","str_NHBB_M_NMT","str_NHBB_H_NMT","str_NHBB_L_MC","str_NHBB_M_MC","str_NHBB_H_MC",
  "str_NHBB_L_CAR","str_NHBB_M_CAR","str_NHBB_H_CAR","str_NHBB_L_PT","str_NHBB_M_PT","str_NHBB_H_PT",
  "str_NHBO_L_NMT","str_NHBO_M_NMT","str_NHBO_H_NMT","str_NHBO_L_MC","str_NHBO_M_MC","str_NHBO_H_MC",
  "str_NHBO_L_CAR","str_NHBO_M_CAR","str_NHBO_H_CAR","str_NHBO_L_PT","str_NHBO_M_PT","str_NHBO_H_PT",
  "str_HBW_L"     ,"str_HBW_M"     ,"str_HBW_H"     ,"str_HBS_L"    ,"str_HBS_M"    ,"str_HBS_H"    ,
  "str_HBO_L"     ,"str_HBO_M"     ,"str_HBO_H"     ,"str_NHBB_L"   ,"str_NHBB_M"   ,"str_NHBB_H"   ,
  "str_NHBO_L"    ,"str_NHBO_M"    ,"str_NHBO_H"    ,"str_HBW"      ,"str_HBS"      ,"str_HBO"      ,
  "str_NHBB"      ,"str_NHBO"      ,"str_L"         ,"str_M"        ,"str_H"        ,"str_ALL"      ,
  "jtp_HBW_L_NMT" ,"jtp_HBW_M_NMT" ,"jtp_HBW_H_NMT" ,"jtp_HBW_L_MC" ,"jtp_HBW_M_MC" ,"jtp_HBW_H_MC" ,
  "jtp_HBW_L_CAR" ,"jtp_HBW_M_CAR" ,"jtp_HBW_H_CAR" ,"jtp_HBW_L_PT" ,"jtp_HBW_M_PT" ,"jtp_HBW_H_PT" ,
  "jtp_HBS_L_NMT" ,"jtp_HBS_M_NMT" ,"jtp_HBS_H_NMT" ,"jtp_HBS_L_MC" ,"jtp_HBS_M_MC" ,"jtp_HBS_H_MC" ,
  "jtp_HBS_L_CAR" ,"jtp_HBS_M_CAR" ,"jtp_HBS_H_CAR" ,"jtp_HBS_L_PT" ,"jtp_HBS_M_PT" ,"jtp_HBS_H_PT" ,
  "jtp_HBW_L"     ,"jtp_HBW_M"     ,"jtp_HBW_H"     ,"jtp_HBS_L"    ,"jtp_HBS_M"    ,"jtp_HBS_H"    ,
  "jtp_HBW"       ,"jtp_HBS"       ,"jtp_L"         ,"jtp_M"        ,"jtp_H"        ,"jtp_ALL"      ,
  "mdl_HBW_L_NMT" ,"mdl_HBW_M_NMT" ,"mdl_HBW_H_NMT" ,"mdl_HBW_L_MC" ,"mdl_HBW_M_MC" ,"mdl_HBW_H_MC" ,
  "mdl_HBW_L_CAR" ,"mdl_HBW_M_CAR" ,"mdl_HBW_H_CAR" ,"mdl_HBW_L_PT" ,"mdl_HBW_M_PT" ,"mdl_HBW_H_PT" ,
  "mdl_HBS_L_NMT" ,"mdl_HBS_M_NMT" ,"mdl_HBS_H_NMT" ,"mdl_HBS_L_MC" ,"mdl_HBS_M_MC" ,"mdl_HBS_H_MC" ,
  "mdl_HBS_L_CAR" ,"mdl_HBS_M_CAR" ,"mdl_HBS_H_CAR" ,"mdl_HBS_L_PT" ,"mdl_HBS_M_PT" ,"mdl_HBS_H_PT" ,
  "mdl_HBO_L_NMT" ,"mdl_HBO_M_NMT" ,"mdl_HBO_H_NMT" ,"mdl_HBO_L_MC" ,"mdl_HBO_M_MC" ,"mdl_HBO_H_MC" ,
  "mdl_HBO_L_CAR" ,"mdl_HBO_M_CAR" ,"mdl_HBO_H_CAR" ,"mdl_HBO_L_PT" ,"mdl_HBO_M_PT" ,"mdl_HBO_H_PT" ,
  "mdl_NHBB_L_NMT","mdl_NHBB_M_NMT","mdl_NHBB_H_NMT","mdl_NHBB_L_MC","mdl_NHBB_M_MC","mdl_NHBB_H_MC",
  "mdl_NHBB_L_CAR","mdl_NHBB_M_CAR","mdl_NHBB_H_CAR","mdl_NHBB_L_PT","mdl_NHBB_M_PT","mdl_NHBB_H_PT",
  "mdl_NHBO_L_NMT","mdl_NHBO_M_NMT","mdl_NHBO_H_NMT","mdl_NHBO_L_MC","mdl_NHBO_M_MC","mdl_NHBO_H_MC",
  "mdl_NHBO_L_CAR","mdl_NHBO_M_CAR","mdl_NHBO_H_CAR","mdl_NHBO_L_PT","mdl_NHBO_M_PT","mdl_NHBO_H_PT",
  "mdl_HBW_L"     ,"mdl_HBW_M"     ,"mdl_HBW_H"     ,"mdl_HBS_L"    ,"mdl_HBS_M"    ,"mdl_HBS_H"    ,
  "mdl_HBO_L"     ,"mdl_HBO_M"     ,"mdl_HBO_H"     ,"mdl_NHBB_L"   ,"mdl_NHBB_M"   ,"mdl_NHBB_H"   ,
  "mdl_NHBO_L"    ,"mdl_NHBO_M"    ,"mdl_NHBO_H"    ,"mdl_HBW"      ,"mdl_HBS"      ,"mdl_HBO"      ,
  "mdl_NHBB"      ,"mdl_NHBO"      ,"mdl_L"         ,"mdl_M"        ,"mdl_H"        ,"mdl_ALL"
  </code></pre>