Use all cores per default, confint tolerance

Also, use more intelligent starting values for the variance of the
random effects for saemix. While this does not appear to speed up
the convergence, it shows where this variance is greatly reduced
by using mixed-effects models as opposed to the separate independent
fits.

1 files changed, 5 insertions, 5 deletions

diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index 38c597b0..16bc2084 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2020-05-11" />
+<meta name="date" content="2020-05-26" />
 
 <title>Example evaluation of FOCUS Example Dataset D</title>
 
@@ -365,7 +365,7 @@ summary {
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">2020-05-11</h4>
+<h4 class="date">2020-05-26</h4>
 
 </div>
 
@@ -439,10 +439,10 @@ print(FOCUS_2006_D)</code></pre>
 <p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABgAAAAMACAMAAADVCFz+AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3deYAU5Z3/8c8Aw4AccqOIIhBFI3FRF/BH0BhFlICQXSOa6PrTCAKiQACFVaNoPIgxCTHqL4nubuJtYhTjgdeqqEk0uoqaTdAVcfFAIyBnSDimf9Vd1TM9Mz3dU1BdT3d9369/5pmq6pnnmdZ6M9NHKQUAMEmuJwAAcCPyAHy0AgBQAh9Ffb6OOgDPVwkAUAJVz0d8wo46ALer4wAAQOQ66vaIT9jRB+CMiL8iAMBzBgEAAJsIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCsFsek37e/N6XzhnYvvvQa9fFNx8AaDkCsFsKBuCSVsrY53cxzggAWooA7JZCAVgoVR07d2pfqfs7cc4JAFqGAOyWAgFY2Vbtlngf/36aND7OOQGoDJtfivTLbfvtzrA3IQB5rL970YctO7JAAKZJ12YGWwdIb0YzMQAJMkVLo/xy1+i2sDcxFIBnpWWpl87Yv6bPmDtrsxs3LTr2gPY9Dpv4ZPD5SJ2WWtJDeirz2QuTPrdH92Fz36vbOS6149+P6tVhyOSV6c9n+X/j13uppmp7q2q1P/y2dEU0SwCQICfrvii/3Gx9P+xNbAXgtauCx2VHrvW3PdAlOIdr1N8zG7wAPF8jPwBbTw/2tV6QCnaO2zBa6u19kZoHUoUDsEwaEQxfk74YzRIAJEixALw/ZEADXyn8Jx4CUIgXgGnqcfMfV949VDpiW3rTcu9c33/qgjkntJZmZA4aqTH91P8bV3yYSu0cJVWPnjt9mHeKPy/YOXZC+5+uSW25trW6eP+8/3DZTdKVy5Zty/Pd7pIuzo47q2s0SwCQIMUC8IAa+UvBwwlAIV4AtH/m+ThbJ0pXpweXSNMzTX2lk/bNHDTSO+jaHZnhQmnIG+nBQ72lJ/yd7dr8IbPvQumO9MfmHwO4QlqUHQ+U1kSzBgDJUSwA92v0ihzdCMBuSAfgXn+4rrN6pf/Z/mXt8Td/yzekzOu1vACc4G/5aw91WeUP/7NKw4Kdk/0tL0tz0x+bD8C3VP+THS7xRFAAjRQPwD/lftqTAOwGLwADsn9Cmyc97n145rHsa7SmSR+nP3rn+Gf8LYuly7M3PV6t1vk7X/U3/EU6P/2x+QCcKz2aHX8l/fAzADRAAIqLMgCnZ8cPSt/J2bXt2T71AVjvb5srPZvdf6n0tL9zs79hTdEATJaWZMdjpFeiWQOA5CAAxUUZgEuy4zekM/3Ru7++bupxHdOPrwQB2CM4ZGLDR18eyOzsGewsHoBvSXdmx8Olt6JZA4DkIADFRRmAH2bHH0lf9T7U/mKQf3rvM7AuAPsEh5zYMAC3ZXb2DXYWD8AC6Ybs+HPS6mjWACA5jtb/OaWQEdon99MajS94+IG6NOwMbAVgbnb8e+k078NU78x+yDcXLVlRO6suANlz/CnSw8vqrUmFC8Ad0mXZcVd1qs1/FAC7BjR+mufu+kbYGdgKwMnZ8W3SnMyW/v7TOlMzmwRgRs4f8QNhAvCqdHwwXC4Nj2IBABJlvOb8spC5Gpb7aWfdWvDwcfpe2BnYCkCXDcF4tNJ/fJsjPRJsGNMkAHfnvIHDQz+5pTYVLgA7e6r6M3+4UPp2NEsAkCA8BlBcpK8DuNIfPid12ph5pk7w9MxVezQJwGcd1Pkjf/heW41tuLN4ANLPA/1ZZlB7uPR6NEsAkCAEoLhIA9DmR+nRki7SRd7HG7J/p//TAd6+/0qP6s/x6eeBDl+eHrw/WPpNw505AbiumW+3olo9MjdfIE2IZgUAkoQAFBdlAPaVDpl0/tBW3oe/eltWdFDVyTfdcc1JVTpYGnTR+gYB2DpEqjlp3mWn1EgXZLY0DcBS6cAbb93Q5HulXSN1nn3bjcdKPd+NZgUAkoQAFBdlAG6eFjxYPvyTzKa7qv1PO/74k97ehw8aBCC17vjg6OqZ/iuImwZgfebdRPO9G6hnfpV/8/1ejmYBABKleAAOWZijAwHYDV4AfpJ66uS9q3uN/kX2LSFWnPMPHbscsWC1N5oydubGhgFIpR75er92vb44fUXwadMApF4Y2anzoI+a+Y4vntW/XbehC/P/ggDAuGIBeKTRszyr1hc8nAAUkglAVDa+xZ91AOyWYgHY9r15uebfW/jLEYBCIg0AAOymk/XrKL/cbP0g7E0IAAA4cevglVF+uUcO/kPYmxAAADCKAESg0fvGpV1Vmu8EANEhABH44aQmHi7NdwKA6BgKAAAgFwEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwKhKCMCYV8JadNvtFv3bD13PwI0bfuZ6Bm581+Z/5rcucj0DN370WOgTYXFjyj4AdwoAUBJ3RnzCjjoAHxx7RFh7qc0eFrVSO9dTcKG9qlxPwYlqVbueghNm/zNvE/pM2AJjPoj4hB11AHbB9zXb9RScGK4XXU/BhZXa3/UUnFioea6n4MTh+i/XU3DhbR3gegotQgCcIQCmEABTCECLEQBTCIAtBKCsEQBnCIApBMAUAtBiBMAUAmALAShrBMAZAmAKATCFALQYATCFANhCAMoaAXCGAJhCAEwhAC1GAEwhALYQgLJGAJwhAKYQAFMIQIsRAFMIgC0EoKwRAGcIgCkEwBQC0GIEwBQCYAsBKGsEwBkCYAoBMIUAtBgBMIUA2EIAyhoBcIYAmEIATCEALUYATCEAthCAskYAnCEAphAAUwhAiy3t/UvXU3DiwoGfup6CC1sHn+V6Ck78Z+8HXE/BiVkHrHM9BRc2f36S6ym0SBkEAADgAgEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAY5TwAL50zsH33odeucz2PWOy8b+L+HdoP+Jcn6jcZWv+L1Xqq7hMb6956y6i+7Q859fn6LSbW/fSZQzvvO3r+X+q3JH3Z27rr45xPmy63XH8ArgNwSStl7PM7xxOJwwdHKjD6k2CTofWv3U/1AbCx7tcPDu7vM7cHWyys++MJwao7L8puSvyyFys3AE2XW7Y/AMcBWChVHTt3al+p+ztuZxKDLV+Q2oyZM+tLVdIXNmY2GVp/7VjVB8DGut/pKfWbMnes9z//HH+LhXVvP1pqO37O5CHe/X27vynxy940ODcATZdbvj8AtwFY2Vbtlngf/36aNN7pTOJwjdT/5fTg+X7SrPTA0voXqj4ARtbt/cL3rzu8j691k36f3mBi3VdKh76XHtzRSh3eTw+SvuztDw5XTgCaLreMfwBuAzBNujYz2DpAetPpVGLg9f8xf/RiG3XYmjK1/ufaqGtdAGys+37pVH90TxB8E+seWLc0b7k/Cj4meNmXDm6b+etOXQCaLreMfwBOA1DbW1Wr/eG3pStcTiUGb0mHZcfeb8nPm1r/J310zKxsAIyse5z0sj/aNmXS5Skj697ZSt2D4b3SBanEL/uY4BGPbACaLrecfwBOA7BMGhEMX5O+6HIqMXhIOic7ni4ttrT+naPUe/XcbABsrHtTjQ5tuMXGuvdU9VZ/dKN0aSrxy156n2dIfQCaLrecfwBOA3CXdHF23FldXU4lBs987WuLs+MJ0uuW1n+5Wj2VqguAjXW/IM1uuMXGuk+VvpcZpJ/08FzKxrJPrA9A0+WW8w/AaQCukOqeKDZQWuNyLrFaVaMuWw2t/8lWWpCqD4CNdf9Uuj71q7F92u419rYdmS021r1qP7U+b9nm1Q8eLk1Lb7Cw7JwANF1uOf8AnAbgW3XPE0ulhktl9gSp0tk0TLoqZWf9H/bScTtzAmBj3ZdIt44L/j582Mr0FhvrTq06Nlh1u+tq059bWHZOAJout5x/AE4DcK70aHb8FWmZy7nE6LeDpCO3pMysf/tR2iv9f0ddAGyse4a0n6pGnDtjVLU0IP3PPhvrTm2cHQRgf/9JbxaWnROApsst5x+A0wBMlpZkx2OkV1zOJTbvfr1KGr4+PTSy/nlq/XT6Y10AbKz7HO8UeMBv06M3Bvt/DLGx7hX7S4Nm3HD16e2kmekNFpadE4Cmyy3nH4DrPwHdmR17vxq95XIuMdk8t633q/FC/50BbKz/4Sp9JzPI/ROQgXVPk2qC53y/Wa2azUbWvaWfdH3mMY9VI5W55y0su+GfgBott5x/AE4DsEC6ITv+nLTa5VziscT7v6P12SuDz0ysf2M3Hb8zM6oLgIl1py6UJmbHJ0hPGFn39dLkYLi6uzqutbHsnAA0XW45/wCcBuAO6bLsuKs61bqcSxxqp8g7KdT/A8DE+j9QQ2cZWXfqB9LV2fFs6edG1j1OWpodT8x0z8KycwLQdLnl/ANwGoBXpeOD4XJpuMupxGKaNPCpnM9NrD9PAEysO/V4zrO/Z0r3Gln3EdL72fHFme5ZWHZOAJout5x/AE4DsLOnqj/zhwulb7ucShwWSV9q8H7gJta/YX7WMOm0+fN/ZWTdqc+qNTo7Plp61ci6x0iPZ8cTMo9+Wlh2TgCaLrecfwBu3wzuXOlnmUHt4ZmXxiba9r7qu77hJlPrr38MwMi6T1TV0/7omSr1Tf/ib2Hd10tfDYZvd1RN+m3PDSw7JwB5llvGPwC3AVhRrR7L04MF0gSnM4nBPdJ3G20ytf6cANhY91Jp78w/hp/eR7o5PbCw7jVdpZmb0qOXPy+dlx4YWHZuAJout4x/AI4vCHON1Hn2bTceK/V81+1MSm+6dNCR9d5Ob7O0/pwAGFn3ZEkjzr/gqCppvP9EKAvrvr+11OuU+VOP9pZ9qP8rb/KXnRuAPMst3x+A60tCzq/yHxrc72XHEym9Exs+GOq/HtDQ+nMDYGPdOyYF93XVuX8LNllY96O9sv+Nn/RpsCnxy24QgDzLLdsfgOsApF48q3+7bkMXbnA9j9I7IF8ADK2/QQCMrPu5swd26DBoyqv1Wyyse/NNY/Zu2+mgSS/Ub0r6shsGIM9yy/UH4DwAAAA3CAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRUQfg438eBQAogX/+uPg5OJSoA3CnAAAlcWfEJ+yoA3C7jnsypCn6WtibAIA5x+n2iE/Y0QfgjLA3+b5mRzwJAEieMwgAANiUyAD8gAAAQFGJDMCL/R6KeBIAkDyJDAAAoDgCAABGEQAAMIoAAIBRBAAAjEpmAP4a8RwAIIESGYDH2vxHxJMAgORJZAB4JTAAFEcAAMAoAgAARhEAADCKAACAUQQAAIwiAABgVCIDwPUAAKC4RAaA6wEAQHGJDAAAoDgCAABGEQAAMIoAAIBRBAAAjEpmALgeAAAUlcgAcD0AACgukQHglcAAUBwBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUYkMANcDAIDiEhkArgcAAMUlMgAAgOIIAAAYRQAAwCgCAABGEQAAMCqZAeB6AABQVCIDwPUAAKC4RAaAVwIDQHEEAACMIgAAYBQBAACjCAAAGEUAAMAoQwFYLi2IYD4AkBCJDED+6wG4CcBj0s/j/64AUFwiA5D/egAEAAByJTIA+REAAMhFAEqMAACV4K0ZH7qewu56a8ZHYW9CAHbJ+rsXtfC/FgIAVIKLtMj1FHbXhfpR2JtYDMDaIdJF+Y54VlqWeumM/Wv6jLmzNrtx06JjD2jf47CJTwafj9RpqSU9pKcyn70w6XN7dB829726neNSO/79qF4dhkxemf58lnzvpQCUszm6vhRf9s0nG1hViu+RNVvfD3sTgwFYd7g0N+8RXgBeu6qVf8oeudbf9kCX4ByuUX/PbPAC8HyN/ABsPT3Y1zr43cILwIbRUm/vi9Q8kCIAQKUoTQD+UKUGOm4uwTfJIgCBvNcDCALw2T+qudeJeQGYph43/3Hl3UOlI7ZlbuSd6/tPXTDnhNbSjMxBIzWmn/p/44oPU6mdo6Tq0XOnD/Pu2POCnWMntP/pmtSWa1ury+pU6sNlN0lXLlu2LfQSAMSpNAF4UD1H5Wir1SX4JlkEwJf/egB+ADZ4Z+tZzdzOC4D2fyc92jpRujo9uESavjM9eKWT9s0cNNI76NodmeFCacgb6cFDvaUn/J3t2vwhs+9C6Y7MXHgMAKgApQrA+NxP9yIAYUX7SuCNR0ozm7tdOgD3+sN1ndUr/c/2L2uPv/lbviGtS3/0AnCCv+WvPdQl+JPef1ZpWLBzsr/l5eDvTAQAqAQEIBplHYBNI6Rpzd7OC8CAncF4nvS49+GZx34XbJgmfZz+6J3jn/G3LJYuz970eLVa5+981d/wF+n89EcCAFQCAhCNcg7A3KPq/lqfjxeA07PjB6Xv5Oza9myf+gCs97fNlZ7N7r9UetrfGTzIs4YAABWEAESjnANQraq2avVSc7fzAnBJdvyGdKY/evfX1009rmP6EfwgAHsEh0xs+Pj+A5mdPYOdBACoJHP0nXXRu1Njcj/tpeUl+CZZ5xGAjGYDoKqfXSMdtqOZ23kB+GF2/JH0Ve9D7S8G+af3PgPrArBPcMiJDQNwW2Zn32AnAQAqyVeUAFPDrtpWAKp+ltr2eTX7ir9nc14g8HvpNO/DVO+Hesg3Fy1ZUTurLgDZc/wp0sPL6q1JEQCgUk1Q+67R66jq3E9bac8SfJOsdv45JwxbAUj/eJZKnZp5GwcvACdnx7dJczJb+vtP60zNbBKAGdKSRl+BAACViccAolEGASh8PYCzpFPy38473XfZEIxHS/el/6PQI8GGMU0CcLd0RfamD/3kltoUAQAqFQGIRhkEoPD1AD7tJj2a93ZeAHSlP3zO+z1hYyo1Of3uQBmr9mgSgM86qHPw7nvvtdXYhjsJAFBJCEA0yiAA+dW9Gdwt0oC8bxaRDkCbzPvpLeniv1/cDdJlmV1/OsDb91/pUf05Pv080OHL04P3B0u/abgzJwDXRTN/AKVDAKJR/gGoHZHzdM9cXgD2lQ6ZdP7QVt6HdCNWdFDVyTfdcc1JVTpYGnTR+gYB2DpEqjlp3mWn1EgXZLY0DcBS6cAbb93Q5HsBKCcEIBrlH4DUG23U9k95jvACcPO04PlUwz/JbLqr2v+0448/6e19+KBBAFLrjg+Orp7pv4K4aQDWZ95NlHcDBcpbqQKwx4AcrQlAWKW4IMyF0jF5jvAC8JPUUyfvXd1r9C+ybwmx4px/6NjliAXe3bZiytiZGxsGIJV65Ov92vX64vQVwadNA5B6YWSnzoNCX6gHQKxKE4DXWjd8on7XvH98jggB2D2ZAERl41vvRva1AJRYiS4I8/GKBj4rxffIIgCBXaxspAEAUEHm6wbXU9hd8/TjsDdJZADyXw+gOAIAWPW/l69xPYXdtQtLSGQA8r8SuDgCAMASswFo9E5uaVcRAACWmA3ADyc18TABAGCJ2QAAgHUEAACMIgAAYBQBAACjEhmA/NcDAADkSmQA8l8PAACQK5EBAAAURwAAwCgCAABGEQAAMIoAAIBRyQxAKa+6AwAJkcgA7Or1AADAkkQGgFcCA0BxBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYlMgBcDwAAiktkALgeAAAUl8gAAACKIwAAYBQBAACjCAAAGEUAAMCoZAaA6wEAQFGJDADXAwCA4hIZAF4JDADFEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABiVyABwPQAAKC6RAeB6AABQXCIDAAAojgAAgFEEAACMIgAAYBQBAACjkhkArgcAAEUlMgBcDwAAiktkAHglMAAURwAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGBUIgPA9QAAoLhEBoDrAQBAcYkMAACgOAIAAEYRAAAwigAAgFEEAACMSmYAuB4AABSVyABwPQAAKC6RAeCVwABQHAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRiQwA1wMAgOISGQCuBwAAxSUyAACA4ggAABhFAADAKAIAAEYRAAAwKpkB4HoAAFBUIgPA9QAAoLhEBoBXAgNAcQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGJTIAXA8AQNlZLi1wPYdGEhkArgcAoOxEEYBt3fVxFHMJJDIAAFB2ogjAYhEAAKg4EQRg02ACELn3ni92xMaHdpR6EgCSbbcDsP3B4SIAkRtR9b9FjpivO0o9CQDJtpsBuHRwW4kARG+w3ixyxHm6qdSTAJBsdQFYO0S6KO8hozQu9enFB+3RbWT6n5xLxvWuOfDUPwf7jpEIQIuEvB5AEIC1B3Zt4OD1dUcQAAC7KRuAdYdLc/Mf4gXg9b7as713nv9W7XSpZyup+il/39L7PEMIQFFhrwcQBOAlNfJa3REEAMBuCgLw2T+q2VcqjdI/9j3sldqd9+8pHaezP0htuUg6oLb+gBMJQFFhXwlcF4DD1+X4AgEAEB0/ABuGSbOaO2SUdODW9OAH3r9Ap2c2fVlaWX8AAShulwMwLHfrEAIAIDqZAGw8UprZ7CFeAB7IDP4sdfL/Bn2d9Hj9AQSgOAIAoOykA7BphDSt+UO8AKzLDDZKX/I33SUtrj+AABRHAACUHS8Ac4+SdF7zh4xSV3+wWTrbH91DAEIiAADKjheAalW1VauXmj1klHr4Ay8Ak/wRAQh7k7AB6KMjR3mGqfOoHJ00vG7cV1eGnQQA5PICoKqfXSMd1uwbCxCAxmIIQMfGz//MY0rYSQBAruXp839q2+elRc0dQgAai+F6AJ/XLU96fqyDnswxUD+pG4/nT0AAdo8XgPO9D0ulTh82cwgBaCyG6wHwGACAksu+Evgs6ZRmDiEAjcX3XkAEAEDpZAPwaTfp0fyHEIDGCACAJKh7M7hbpAH537CMADRGAAAkQV0AakdIl+Q9hAA0FmcA9p6XoxcBABCd+usBvNFGbf+U7xAC0Fh8Afhj46d+vl13BAEAsJtyLghzoXRMvkMIQGPxXQ8g9cuFDfy6/ggCAMCJ2k/fXFuyL57IAIS9HsAX9MciR5ynm8NOAgDKXCIDEPaVwNeN21rkiCe++OciRwBApSEAAGAUAQCA+J3Y9B3Hrop9EgQAAOL3w0lNPBz7JAgAABhFAADAKAIAAEYlMgBhrwcAABYlMgBhrwcAABYlMgAAgOIIAAAYRQAAwCgCAABGEQAAMCqZAQh5PQAAsCiRAQh7PQAAsCiRAeCVwABQHAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRiQwA1wMAgOISGQCuBwAAxSUyAACA4ggAABhFAADAKAIAAEYRAAAwKpkB4HoAAFBUIgPA9QAAoLhEBoBXAgNAcQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGJTIAXA8AAIpLZAC4HgAAFJfIAAAAiiMAAGAUAQAAowgAABhFAADAqGQGgOsBAEBRiQwA1wMAgOISGQBeCQwAxREAADCKAACAUQQAAIwiAABgFAEAAKMqIQAT14V0lc551463/sf1DOL0J9cTiJOpxf6Pqf+OV4Q9qZXIxAoIAACgJMo9AMv7dQ2rRqqyg8UmlqnVGlts+9CntdLotzziE3bUAdgFph4D+ES9XE8hRqP1uOspxOcXOtP1FOKzU61cTyFG39CdrqdQKgQgXgQgsQhAYhGAUiIAiUUAkooAJAQBiBcBSCwCkFgEoJQIQGIRgKQiAAlBAOJFABKLACQWASglApBYBCCpCEBCEIB4EYDEIgCJRQBKiQAkFgFIKgKQEAQgXgQgsQhAYhGAUiIAiUUAkooAJAQBiBcBSCwCkFgEoJQIQGIRgKQiAAlBAOJFABKLACQWASglApBYBCCpCEBCEIB4EYDEIgCJRQBKiQAkFgFIKgKQEAQgXgQgsQhAYhGAUnqw6mbXU4jP33qNcD2FGJ3XNuor2JWxF1pd6XoKMRp4oOsZxOiyVr93PYVSKYMApP7iegJx2rjV9QxitHON6xnEydR/x5u3uJ5BnJJ715ZDAAAADhAAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADAqvgC8dM7A9t2HXruuRbsKHFwRwix2530T9+/QfsC/PBHf9CIW6q7NeLFaT8UxsxIItditt4zq2/6QU5+PbXYRC7XYp88c2nnf0fP/Etvsolb4rLOtuz5u8cEVI7YAXNJKGfv8rgW7ChxcEcIs9oMjFRj9SbyzjEqouzZj7X6q1ACEWuzrBwd37ZnbY51kVMIs9uMJwVo7L4p3kpEpctZZrNwAVPopKiuuACyUqo6dO7Wv1P2dorsKHFwRwix2y6opgV4AABoPSURBVBekNmPmzPpSlfSFjfHPdfeFumszaseqUgMQarHv9JT6TZk71jtXzIl9phEIs9jtR0ttx8+ZPMS7a2+Pf6oRKHLW2TQ4NwCVfoqqE1MAVrZVuyXex7+fJo0vtqvAwRUh1GKvkfq/nB4830+aFfNMoxBqtb6FqtQAhFus97vdv+7wPr7WTfp9zDONQKjFXikd+l56cEcrdXg/5plGofBZZ/uDw5UTgEo/RdWLKQDTpGszg60DpDeL7CpwcEUItVjv3xCP+ftebKMOW2OcZkRCrTbjuTbqWqEBCLXY+6VT/X33VGTbQy12YN0h3r4fxTjLqBQ661w6uG3m7z0ft+TgyhJPAGp7q2q1P/y2dEXhXQUOrgihFvuWdFh2r/dLdOU9WhhqtRmf9NExsyozAOEWO0562d+ybcqky2ObZFRCLXZnK3UPdt4rXRDfLKNS8KxzTPDwxsctObiyxBOAZdKIYPia9MXCuwocXBFCLfYh6Zzs3unS4pjmGJ1Qq03bOUq9V8+tzACEWuymGh0a6+wiFu6e3VPVwa+vN0qXxjXH6BQ86yy9zzOkPgCVforKEU8A7pIuzo47q2vhXQUOrgihFvvM175Wd9KfIL0eywyjFGq1aZer1VOpCg1AqMW+IM2OcW6RC3fPnip9L/N5+lkNz8U0xQgVP+ucWB+ASj9F5YgnAFdIdc8NGyitKbirwMEVIdRic6yqUZfKewwg7GqfbKUFqUoNQKjF/lS6PvWrsX3a7jX2th2xTjMa4e7ZVfup9XnLNq9+8HBpWpzTjEjxs05OACr9FJUjngB8K+epYcOldwruKnBwRQi12HqbhklXxTC9iIVc7Ye9dNzOig1AqMVeIt06Lvjj8WEr45xmNELes6uODdba7rraGGcZleJnnZwAVPopKkc8AThXejQ7/oq0rOCuAgdXhFCLrfPbQdKRW+KYX7TCrXb7Udor/X9RhQYg1GJnSPupasS5M0ZVSwMq71+JIf873jg7CMD+j8U4ycgUP+vkBKDST1E54gnAZGlJdjxGeqXgrgIHV4RQiw28+/Uqafj6eCYYqXCrnafWT6c/rdAAhFrsOd7J8IDfpj9/Y3Al/lkk3D27Yn9p0Iwbrj69nTQzzmlGpPhZJycAlX6KyhHbn4DuzI69X5neKrirwMEVIdRiMzbPbev95rywIt8uINRqH67SdzKfVmgAQi12mlQTPEX8zWrVbI5tlhEJtdgt/aTrM490rBqp4E6uKMXPOg3/BFTRp6gc8QRggXRDdvw5aXXBXQUOrgihFpu2xPufp/XZK+OaX7TCrHZjNx2/M/NphQYg1F17oTQxu+UEqeLe6y/UYq+XJgcbVndXx7VxTTIyxc86OQGo9FNUjngCcId0WXbcVZ1qC+4qcHBFCLXYVKp2irwzRcX+IyLMaj9QQ2fFOtMIhLprfyBdnd0yW/p5THOMTKjFjpOWZrdMrMDateCskxOASj9F5YgnAK9KxwfD5dLwwrsKHFwRQi0285eCgZX4r+FAmNVWfABC3bWP5zxZfKZ0b0xzjEyoxR4h1b0B0MUVWLsWnHVyAlDpp6gc8QRgZ09Vf+YPF0rfLryrwMEVIdRiU4ukL1Xye4qHWe2G+VnDpNPmz/9V3JPdXaHu2s+qNTq792jp1dhmGZFQix0jPZ7dOyHnIdKKUfyskxOASj9F5YjpzeDOlX6WGdQe3vjlrk13FTi4IoRZ7Pa+6luJT/6pF+quzarQxwDCLfZEVT3t73umSn0r7+8EYRZ7vfTVYN/bHVVTge9rXvSskxOAij9F1YspACuq1WN5erBAmpDZsm7x4sVb8+9quqWyhFnsPdJ3HU0zIqHu2qxKDUCoxS6V9s78s/jpfaSbXUx394RZ7Jqu0sxN6dHLn5fOczLf3VNgtb7cAFT6KapeXBeEuUbqPPu2G4+Ver6b2fCipA/y78qzpbKEWOx06aAj673tbtK7LNRdG6jUAIRb7GRv34jzLziqShq/09WMd0OYxd7fWup1yvypR3uLPbQif6ctsNqM3ABU/CmqTmyXhJxf5T/wt1/wDrk5P93Gu/JtqSwtX+yJDR8WrcjXFIa6a30VG4BQi90xKbhbq879m5PJ7q4wi320V/Y/4pM+dTLZ3VZgtWkNAlDxp6is+C4K/+JZ/dt1G7pwQ/bTnJ9uo115t1SWFi/2gAQEINxdm1G5AQi32OfOHtihw6ApFfcAcFaYxW6+aczebTsdNOmF2GcZlQKrTTUOQMWfogLxBQAAUFYIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFFRB2DjhecCAErg0qgvthl1AO4RAKAk7on4hB11AG7X8J+GdIpGhb0JAFS2UTol7E2G6/aIT9jRB+CMsDf5vmZHPAkAKHOz9f2wNzmDAABAAhAAHwEAYA4B8BEAAOYQAB8BAGAOAfDdqgURTwIAytwC3Rr2JokMwLaHtkQ8CQAoc1se2hb2JokMAACgOAIAAEYRAAAwigAAgFEEAACMSmQAPrxgecSTAIAyt/yCD8PeJJEB+JEujHgSAFDmLtSPwt4kkQHglcAAzOGVwD4CAMAcAuAjAADMIQA+AgDAHALgIwAAzCEAPgIAwBwC4CMAAMwhAD6uBwDAHK4H4ON6AADM4XoAAICWIgAAYBQBAACjCAAAGEUAAMCoRAaA6wEAMIfrAfi4HgAAc7gegI9XAgMwh1cC+wgAAHMIgI8AADCHAPgIAABzCICPAAAwhwD4CAAAcwiAjwAAMIcA+LgeAABzuB6Aj+sBADCH6wEAAFqKAACAUQQAAIwiAABgFAEAAKMSGQCuBwDAHK4H4ON6AADM4XoAPl4JDMAcXgnsIwAAzCEAPgIAwBwC4CMAAMwhAD4CAMAcAuBrLgA7bzm8Y68/7PaMAKD8EABfcwFYIM+zuz2jlnlM+nn4XbtgW3d9HN1XA1ChCICvmesBbK9Rp1m3xXWyjC0Ai0UAAHA9gEAz1wNYLs3b/fm0VFwB2DSYAADgegBFLJMWRvSlWiCeAGx/cLgIQBw+O+vxuL/ljpn/Fve3hDEEIJz1dy9q4dttxBGASwe3TT+sQQBi8JD+Ke5v+a4OjPtbwhg7AVgkX74HgZ+VlqVeOmP/mj5j7qzNbty06NgD2vc4bOKTwecjdVpqSQ/pqcxnL0z63B7dh819r27nuNSOfz+qV4chk1emP58VfLf3Uk002LXjF+P7tu122EUrm533KO8rf3rxQXt0G3mH99mScb1rDjz1z8G+Y4IvRQBKb7EmhL3Jp/f9MtdDm0Pe/n/0ubDfEgiFAKR5AXjtqlb+/pFr/W0PdAluoFF/z2zwAvB8jfwAbD092Nc6eLjZC8CG0VJv74vUPJBqcQBWDA4+qbm+uXl7AXi9r/Zs7x30rdrpUk/vW1T7EUotvc8zhADEYRcCME4NhX11CgFAqdkJwKfL7pVmLFuW759hXgCmqcfNf1x591DpiMwDKcu9c33/qQvmnNDau1XmoJEa00/9v3HFh6nUzlHeSXj03OnDvP+rzwt2jp3Q/qdrUluuba0uq1OpD5fdJF25bFmeB2Vydq3eS+p0wrz/O8j7Otc1M+9R+se+h71Su/P+PaXjdPYHqS0XSQfU1h9wIgGIwy4EYISOPaXeUJ0V8vYEAKWWyAA0cz2A5h8D8AKg/d9Jj7ZOlK5ODy6Rpu9MD17ppH0zB430Drp2R2a4UBryRnrwUG/pCX9nuzb+S8wulNJ/q2nRYwATpMNXeh9rvWxU/zn/wV5rDtyaHvzA+/7TM5u+LK2sP4AAxGKXAvDbnM/+gwCgtLgegK+Z6wEUDsC9/nBdZ/VK/7P9y9rjb/6Wb0jr0h+9AJzgb/lrD3VZ5Q//s0rDgp2T/S0vS3PTH1sQgD9XqdsGf9N8aUr+g70APJAZ/Nn7bWF9ZnSdlPOMFAIQCwKAcsf1AHzNvBK4YAAG7AzG8/yz6zOP/S7YMC04wXrn+Gf8LYuly7M3PV6t1vk7X/U3/EU6P/2xBQG4JvhtI32rDuqV/+BRQX9SG6Uv+ZvukhbXH0AAYkEAUO54JbBvVwJwenb8oPSdnF3bnu1THwD/H+CpuTkPJV8qPe3vDB5cWNPyAIyX3shuO67Bn3VyjFJXf7BZOtsf3UMA4kcAUO4IgG9XAnBJdvyGdKY/evfX1009rmPd0yxHao/gkIkNn9zxQGZnz2BniAAMlTZmt02SXsh78Cj18AdeACb5IwLgwGKNWhHS4fpVzmfX6eSQt3+aACAMAuDblQD8MDv+SPqq96H2F4P803ufgXUB2Cc45MSGAbgts7NvsDNEAA5Sx7ptl0sP5z2YAJSHG+RAB9erRiUhAL5dCcDc7Pj30mneh6ne/36HfHPRkhW1s+oCkD3Hn+KdrJfVW5PaxQB4vwFsym7zvt/SvAcTgPLwb+owIKR26pPzWU91Cnn7fbN//QNaggD4diUAJ2fHt0lzMlv6B1cOmNkkADOkJY2+wi4FYLz0Znbb8dJbeQ8mAOWBxwBQ7giAb1cC0CV4PmZqtHRfKjVHeiTYMKZJAO6Wrsje9KGf3FKb2sUAXF0/obWd1a0278EEoDwQAJQ7AuBr5noAhV8HcKU/fE7qtDGVmpx+d6CMVXs0CcBnHdT5I3/4XluNbbgzRAD+W+oR/A1oft1TfBojAOWBAKDccT0AXzPXAygcgDaZ11As6SJdlMo85HdZZtefDvD2/Vd6VH+OTz8PdHjmxcbvD5Z+03BnTgCae3eHul3jpCPfTw++21rV/53/YAJQHggAyh3XAyioYAD2lQ6ZdP7QVt6Hv3pbVnRQ1ck33XHNSVU6WBp00foGAdg6RKo5ad5lp9RIF2S2NA3AUunAG2/d0OR75e76sJe050mXTj5Ehd4LiACUAwKABCIAaV4Abp4WPPNu+CeZTXdV+592/PEnvb0PHzQIQGrd8cHR1TP9VxA3DcD6zLuJ5nk30Nxdbx8cfJ2aZv94RwDKwy4FoFPXeh0IAMoOAUjzAvCT1FMn713da/Qvsm8JseKcf+jY5YgFq73RlLEzNzYMQCr1yNf7tev1xekrgk+bBiD1wshOnQd9lPf71e/a/u/j+rTtMuSivKHIIADlYRcCMLnRs/q/F/L2BAClZikAzcsEICob33o3sq+VX+2nb64t8bdAY7sQgNp1DawPe3sCgFIjAGmRBgCJ9JhOjftbvq/BcX9LGJPIADRzPYDmEQAUs+P/vRP797z7pdi/JSoY1wPwNXM9gOYRAACVjusB+Jp5JbCv0Tu5pV1VugDk+27RHAwAuXglsK9gAH44qYmHSxeAfN8tmoMBIBcB8BUMAAAkEQHwEQAA5hAAHwEAYA4B8BEAAOYQAB8BAGAOAfA1cz0AAEgurgfga+Z6AACQXFwPAADQUgQAAIwiAABgFAEAAKMIAAAYlcgAhL4eAABUOq4H4At9PQAAqHRcD8DHK4EBmMMrgX0EAIA5BMBHAACYQwB8BACAOQTARwAAmEMAfAQAgDkEwEcAAJhDAHxcDwCAOVwPwMf1AACYw/UAAAAtRQAAwCgCAABGEQAAMIoAAIBRiQwA1wMAYA7XA/BxPQAA5nA9AB+vBAZgDq8E9hEAAOYQAB8BAGAOAfARAADmEAAfAQBgDgHwEQAA5hAAHwEAYA4B8HE9AADmcD0AH9cDAGAO1wMAALQUAQAAowgAABhFAADAKAIAAEYlMgBcDwCAOVwPwMf1AACYw/UAfLwSGIA5vBLYRwAAmEMAfAQAgDkEwEcAAJhDAHwEAIA5BMBHAACYk9AA9DslpAPVoa9h3Tu7noFL+3Tq6XoKLvXstI/rKbjUubvrGbjUQQeGPVf2K/sA/EYAgJL4TcQn7KgDUPvIL8M6Q0d8y7A99U3XU3Doq+rvegou9ddXXU/BoW9qT9dTcOkInRH6ZPlIbcQn7KgDsAuMPwYwQCtcT8GhR/QV11Nw6St6xPUUHFqhAa6n4NIuPAYQPQLgGgEwjAAYRgB8BMD1FBwiAK6n4BABcD0FAuAeATCMABhGAHwEwPUUHCIArqfgEAFwPQUC4B4BMIwAGEYAfATA9RQcIgCup+AQAXA9BQLgHgEwjAAYRgB8BMD1FBwiAK6n4BABcD0FAuAeATCMABhGAHwEwPUUHCIArqfgEAFwPQUC4B4BMIwAGEYAfATA9RQcIgCup+AQAXA9BQLgHgEwjAAYRgB8BMD1FBwiAK6n4BABcD0FAuAeATCMABhGAHwEwPUUHCIArqfgEAFwPYWyCMCPNc/1FFwapPddT8GhJzXB9RRcmqAnXU/Bofc1yPUUXJqnH7ueQlkEYO2CVa6n4NITN7uegUvbrnnN9RRceu2aba6n4NLNT7iegUurFqx1PYWyCAAAwAUCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABhFAADAKAIAAEYRAAAwigAAgFEEAACMIgAAYBQBAACjCAAAGEUAAMAoAgAARhEAADCKAACAUQQAAIwiAABgFAEAAKMIAAAYRQAAwCgCAABGOQjAS+cMbN996LXrwu1KjObXuPO+ift3aD/gX56If1KxKXYPv1itp+KcT8wKLH/rLaP6tj/k1Odjn1N8Cqz+6TOHdt539Py/xD6neG3rro/zbXd34os/AJe0UsY+vwuzKzGaX+MHRyow+hMXM4tDsXt47X5KcgAKLP/1g4M7/8ztDiYWi+ZX//GEYPGdF7mYWHwWK28AHJ74Yg/AQqnq2LlT+0rd32n5rsRofo1bviC1GTNn1peqpC9sdDO7Uit2D9eOVZIDUGD57/SU+k2ZO9Y7E8xxMrfSa37124+W2o6fM3mId+/f7mZy8dg0OG8AXJ744g7AyrZqt8T7+PfTpPEt3pUYBdZ4jdT/5fTg+X7SLAdzK72i9/BCJTkAhZbv/fb3rzu8j691k37vYG6lV2D1V0qHvpce3NFKHd53MLd4bH9wuPIFwOmJL+4ATJOuzQy2DpDebOmuxCiwRq//j/mjF9uow9a4ZxaHYvfwc23UNcEBKLD8+6VT/dE9Sa1/gdUPrNvgHfSjuCcWk0sHt838madpAJye+GIOQG1vVa32h9+WrmjhrsQosMa3pMOyY+834iQ+FljsHv6kj46ZldwAFFr+OOllf7RtyqTL451XPAqsfmcrdQ+G90oXxDyxuBwTPMzRJABuT3wxB2CZNCIYviZ9sYW7EqPAGh+SzsmOp0uLY51XPIrcwztHqffquckNQIHlb6rRoQ5mFKdCd/6eqg5+471RujTeecVm6X2eIXkC4PbEF3MA7pIuzo47q2sLdyVGgTU+87Wv1Z30J0ivxzmtmBS5hy9Xq6dSCQ5AgeW/IM2Of0KxKnTnnyp9LzNIPxHiuXjnFa8T8wTA7Ykv5gBcIdU90WugtKZluxKjZWtcVaMuSXwMoPDqn2ylBakkB6DA8n8qXZ/61dg+bfcae9sOB1OLQaE7f9V+an3ess2rHzxcmhb/1GKULwBuT3wxB+BbOc/zGi6907JdidGiNW4aJl0V25RiVHD1H/bScTsTHYACy79EunVc8Dfiw1bGP7UYFLzzVx0bLL7ddbWxzyxO+QLg9sQXcwDOlR7Njr8iLWvZrsRoyRp/O0g6ckt8c4pPodVvP0p7pf/XSHAACix/hrSfqkacO2NUtTQgkb/8FvxPf+PsIAD7Pxb7xGKVLwBuT3wxB2CytCQ7HiO90rJdiVF8je9+vUoavj7OScWm0OrnqfXT6Y8JDkCB5Z/jnfsO+G169MbghP4VpNCdv2J/adCMG64+vZ00M/6pxShfANye+OL/E9Cd2bH3685bLduVGMXWuHluW+/X4IUJfTOAAqt/uErfyQwSHIACy58m1QRPAH+zWjWbY55ZHAqsfks/6frMQx+rRir47yChmvkTkMMTX8wBWCDdkB1/Tlrdsl2JUWSNS7z/E1qfvTLeOcWn+dVv7Kbjd2ZGCQ5AgTv/QmlidnyClMR3Ayyw+uulycFwdXd1XBvvxGKVLwBuT3wxB+AO6bLsuKs61bZsV2IUXGPtFHnngUT+5uNrfvUfqKGzXEyv1Arc+T+Qrs6OZ0s/j3Ve8Siw+nHS0ux4YjLzl5UvAG5PfDEH4FXp+GC4XBrewl2JUXCN06SBSf3Xb0bzqzcRgAJ3/uM5TwWfKd0b67ziUWD1R0h1bwB0cTLzl5UvAG5PfDEHYGdPVX/mDxdK327hrsQotMZF0pcSfSGEAqvfMD9rmHTa/Pm/cjK/Eitw539WrdHZ8dHSq/FOLBYFVj9Gejw7npDziGgC5QuA2xNf3G8Gd670s8yg9vDGL3ctsCsxml/j9r7qm8wn/9RrwT2c4McACi3/RFU97Y+eqVLfRP75s/nVXy99NRi+3VE1CX0r9Ix8AXB74os7ACuq1WN5erBAmpDZsm7x4sVb8+9KnuaXf4/0XZczi0OBOz8ryQEosPyl0t6ZfwU/vY90s7MZllLzq1/TVZq5Kb3l5c9L57mbYunlBqA8TnyxXxDmGqnz7NtuPFbq+W5mw4uSPsi/K4GaXf506aAj673tdpolUuDODyQ5AIWWP9kbjTj/gqOqpPE7Xc6xdJpf/f2tpV6nzJ96tLf6QxP9a3BuAMrjxBf/JSHnV/kP9O0XvP9tzjmg8a4kam75JzZ8GDSRL4QueOf7Eh2AAsvfMSm446vO/ZvDCZZU86t/tFf2v/uTPnU4wdLLHwCXJz4HF4V/8az+7boNXbgh+2nOOaDRrkRqZvkHmAhAoTs/I9kBKLT8584e2KHDoClJfAA4q/nVb75pzN5tOx006QVnc4tHMwFweOJzEAAAQDkgAABgFAEAAKMIAAAYRQAAwCgCAABGEQAAMIoAAIBRBAAAjCIAAGAUAQAAowgAABj1/wFiW+t1g5aN+QAAAABJRU5ErkJggg==" width="768" /></p>
 <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <pre class="r"><code>summary(fit)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50 
+<pre><code>## mkin version used for fitting:    0.9.50.3 
 ## R version used for fitting:       4.0.0 
-## Date of fit:     Mon May 11 04:41:12 2020 
-## Date of summary: Mon May 11 04:41:12 2020 
+## Date of fit:     Tue May 26 17:01:07 2020 
+## Date of summary: Tue May 26 17:01:07 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent