CRAN Package Check Results for Package popdemo

Last updated on 2021-11-28 19:49:00 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.3-1 OK
r-devel-linux-x86_64-debian-gcc 1.3-1 9.79 96.71 106.50 OK
r-devel-linux-x86_64-fedora-clang 1.3-1 172.59 NOTE
r-devel-linux-x86_64-fedora-gcc 1.3-1 158.03 OK
r-devel-windows-x86_64-new-UL 1.3-1 30.00 178.00 208.00 NOTE
r-devel-windows-x86_64-new-TK 1.3-1 OK
r-devel-windows-x86_64-old 1.3-1 19.00 159.00 178.00 NOTE
r-patched-linux-x86_64 1.3-1 13.23 116.13 129.36 OK
r-patched-solaris-x86 1.3-1 161.90 NOTE
r-release-linux-x86_64 1.3-1 12.16 114.47 126.63 OK
r-release-macos-arm64 1.3-1 NOTE
r-release-macos-x86_64 1.3-1 NOTE
r-release-windows-ix86+x86_64 1.3-1 27.00 115.00 142.00 NOTE
r-oldrel-macos-x86_64 1.3-0 NOTE
r-oldrel-windows-ix86+x86_64 1.3-0 20.00 120.00 140.00 ERROR

Check Details

Version: 1.3-1
Check: installed package size
Result: NOTE
     installed size is 5.4Mb
     sub-directories of 1Mb or more:
     doc 4.9Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-x86_64-new-UL, r-devel-windows-x86_64-old, r-patched-solaris-x86, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-ix86+x86_64

Version: 1.3-0
Check: installed package size
Result: NOTE
     installed size is 5.4Mb
     sub-directories of 1Mb or more:
     doc 4.9Mb
Flavors: r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.3-0
Check: examples
Result: ERROR
    Running examples in 'popdemo-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: Projection-class
    > ### Title: 'Projection' object S4 class
    > ### Aliases: Projection-class Projection-class Projection vec
    > ### vec,Projection-method bounds bounds,Projection-method mat
    > ### mat,Projection-method Aseq Aseq,Projection-method projtype
    > ### projtype,Projection-method vectype vectype,Projection-method nproj
    > ### nproj,Projection-method nmat nmat,Projection-method ntime
    > ### ntime,Projection-method
    >
    > ### ** Examples
    >
    > ### USING PROJECTION OBJECTS
    >
    > # Create a 3x3 PPM
    > ( A <- matrix(c(0,1,2,0.5,0.1,0,0,0.6,0.6), byrow=TRUE, ncol=3) )
     [,1] [,2] [,3]
    [1,] 0.0 1.0 2.0
    [2,] 0.5 0.1 0.0
    [3,] 0.0 0.6 0.6
    >
    > # Project stage-biased dynamics of A over 70 intervals
    > ( pr <- project(A, vector="n", time=70) )
    3 deterministic population projections (stage-biased initial vectors) over 70 time intervals.
    
     biasS1 biasS2 biasS3
     [1,] 1.000000e+00 1.000000e+00 1.000000e+00
     [2,] 5.000000e-01 1.700000e+00 2.600000e+00
     [3,] 8.500000e-01 2.230000e+00 2.560000e+00
     [4,] 1.115000e+00 2.609000e+00 3.236000e+00
     [5,] 1.304500e+00 3.317500e+00 4.171600e+00
     [6,] 1.658750e+00 4.139210e+00 5.111960e+00
     [7,] 2.069605e+00 5.139847e+00 6.384676e+00
     [8,] 2.569924e+00 6.414395e+00 7.970016e+00
     [9,] 3.207198e+00 7.993372e+00 9.921856e+00
    [10,] 3.996686e+00 9.959649e+00 1.236751e+01
    [11,] 4.979824e+00 1.241316e+01 1.541388e+01
    [12,] 6.206578e+00 1.546947e+01 1.920798e+01
    [13,] 7.734733e+00 1.927831e+01 2.393794e+01
    [14,] 9.639155e+00 2.402533e+01 2.983223e+01
    [15,] 1.201266e+01 2.994103e+01 3.717765e+01
    [16,] 1.497051e+01 3.731336e+01 4.633192e+01
    [17,] 1.865668e+01 4.650100e+01 5.774018e+01
    [18,] 2.325050e+01 5.795089e+01 7.195746e+01
    [19,] 2.897544e+01 7.222007e+01 8.967548e+01
    [20,] 3.611003e+01 9.000274e+01 1.117562e+02
    [21,] 4.500137e+01 1.121640e+02 1.392738e+02
    [22,] 5.608201e+01 1.397820e+02 1.735670e+02
    [23,] 6.989102e+01 1.742004e+02 2.163042e+02
    [24,] 8.710020e+01 2.170936e+02 2.695646e+02
    [25,] 1.085468e+02 2.705483e+02 3.359391e+02
    [26,] 1.352741e+02 3.371651e+02 4.186571e+02
    [27,] 1.685826e+02 4.201849e+02 5.217425e+02
    [28,] 2.100925e+02 5.236466e+02 6.502106e+02
    [29,] 2.618233e+02 6.525835e+02 8.103113e+02
    [30,] 3.262917e+02 8.132684e+02 1.009833e+03
    [31,] 4.066342e+02 1.013519e+03 1.258483e+03
    [32,] 5.067593e+02 1.263076e+03 1.568358e+03
    [33,] 6.315381e+02 1.574082e+03 1.954534e+03
    [34,] 7.870410e+02 1.961666e+03 2.435796e+03
    [35,] 9.808332e+02 2.444685e+03 3.035560e+03
    [36,] 1.222343e+03 3.046638e+03 3.783002e+03
    [37,] 1.523319e+03 3.796808e+03 4.714487e+03
    [38,] 1.898404e+03 4.731692e+03 5.875330e+03
    [39,] 2.365846e+03 5.896771e+03 7.322006e+03
    [40,] 2.948386e+03 7.348726e+03 9.124895e+03
    [41,] 3.674363e+03 9.158195e+03 1.137171e+04
    [42,] 4.579098e+03 1.141321e+04 1.417175e+04
    [43,] 5.706604e+03 1.422347e+04 1.766125e+04
    [44,] 7.111735e+03 1.772570e+04 2.200996e+04
    [45,] 8.862849e+03 2.209028e+04 2.742944e+04
    [46,] 1.104514e+04 2.752954e+04 3.418336e+04
    [47,] 1.376477e+04 3.430811e+04 4.260030e+04
    [48,] 1.715406e+04 4.275576e+04 5.308972e+04
    [49,] 2.137788e+04 5.328346e+04 6.616194e+04
    [50,] 2.664173e+04 6.640339e+04 8.245293e+04
    [51,] 3.320170e+04 8.275383e+04 1.027552e+05
    [52,] 4.137691e+04 1.031302e+05 1.280565e+05
    [53,] 5.156511e+04 1.285238e+05 1.595877e+05
    [54,] 6.426192e+04 1.601701e+05 1.988829e+05
    [55,] 8.008507e+04 1.996087e+05 2.478536e+05
    [56,] 9.980433e+04 2.487581e+05 3.088823e+05
    [57,] 1.243790e+05 3.100095e+05 3.849380e+05
    [58,] 1.550047e+05 3.863428e+05 4.797209e+05
    [59,] 1.931714e+05 4.814716e+05 5.978420e+05
    [60,] 2.407358e+05 6.000238e+05 7.450480e+05
    [61,] 3.000119e+05 7.477670e+05 9.285004e+05
    [62,] 3.738835e+05 9.318888e+05 1.157124e+06
    [63,] 4.659444e+05 1.161347e+06 1.442041e+06
    [64,] 5.806734e+05 1.447304e+06 1.797114e+06
    [65,] 7.236519e+05 1.803672e+06 2.239615e+06
    [66,] 9.018360e+05 2.247788e+06 2.791073e+06
    [67,] 1.123894e+06 2.801258e+06 3.478316e+06
    [68,] 1.400629e+06 3.491009e+06 4.334777e+06
    [69,] 1.745505e+06 4.350597e+06 5.402125e+06
    [70,] 2.175298e+06 5.421839e+06 6.732284e+06
    [71,] 2.710920e+06 6.756853e+06 8.389967e+06
    > plot(pr)
    >
    > # Access other slots
    > vec(pr) #time sequence of population vectors
    , , biasS1
    
     S1 S2 S3
     [1,] 1.000000e+00 0.000000e+00 0.000000e+00
     [2,] 0.000000e+00 5.000000e-01 0.000000e+00
     [3,] 5.000000e-01 5.000000e-02 3.000000e-01
     [4,] 6.500000e-01 2.550000e-01 2.100000e-01
     [5,] 6.750000e-01 3.505000e-01 2.790000e-01
     [6,] 9.085000e-01 3.725500e-01 3.777000e-01
     [7,] 1.127950e+00 4.915050e-01 4.501500e-01
     [8,] 1.391805e+00 6.131255e-01 5.649930e-01
     [9,] 1.743112e+00 7.572151e-01 7.068711e-01
    [10,] 2.170957e+00 9.472773e-01 8.784517e-01
    [11,] 2.704181e+00 1.180206e+00 1.095437e+00
    [12,] 3.371081e+00 1.470111e+00 1.365386e+00
    [13,] 4.200883e+00 1.832552e+00 1.701298e+00
    [14,] 5.235148e+00 2.283697e+00 2.120310e+00
    [15,] 6.524317e+00 2.845944e+00 2.642404e+00
    [16,] 8.130752e+00 3.546753e+00 3.293009e+00
    [17,] 1.013277e+01 4.420051e+00 4.103857e+00
    [18,] 1.262777e+01 5.508390e+00 5.114345e+00
    [19,] 1.573708e+01 6.864722e+00 6.373641e+00
    [20,] 1.961200e+01 8.555012e+00 7.943018e+00
    [21,] 2.444105e+01 1.066150e+01 9.898818e+00
    [22,] 3.045914e+01 1.328667e+01 1.233619e+01
    [23,] 3.795906e+01 1.655824e+01 1.537372e+01
    [24,] 4.730568e+01 2.063535e+01 1.915917e+01
    [25,] 5.895370e+01 2.571637e+01 2.387672e+01
    [26,] 7.346981e+01 3.204849e+01 2.975585e+01
    [27,] 9.156020e+01 3.993975e+01 3.708261e+01
    [28,] 1.141050e+02 4.977407e+01 4.621341e+01
    [29,] 1.422009e+02 6.202989e+01 5.759249e+01
    [30,] 1.772149e+02 7.730344e+01 7.177343e+01
    [31,] 2.208503e+02 9.633778e+01 8.944612e+01
    [32,] 2.752300e+02 1.200589e+02 1.114703e+02
    [33,] 3.429996e+02 1.496209e+02 1.389176e+02
    [34,] 4.274560e+02 1.864619e+02 1.731231e+02
    [35,] 5.327081e+02 2.323742e+02 2.157510e+02
    [36,] 6.638762e+02 2.895914e+02 2.688751e+02
    [37,] 8.273417e+02 3.608972e+02 3.350799e+02
    [38,] 1.031057e+03 4.497606e+02 4.175863e+02
    [39,] 1.284933e+03 5.605046e+02 5.204081e+02
    [40,] 1.601321e+03 6.985170e+02 6.485476e+02
    [41,] 1.995612e+03 8.705121e+02 8.082388e+02
    [42,] 2.486990e+03 1.084857e+03 1.007251e+03
    [43,] 3.099358e+03 1.351981e+03 1.255265e+03
    [44,] 3.862510e+03 1.684877e+03 1.564347e+03
    [45,] 4.813572e+03 2.099743e+03 1.949535e+03
    [46,] 5.998812e+03 2.616760e+03 2.429567e+03
    [47,] 7.475893e+03 3.261082e+03 3.027796e+03
    [48,] 9.316674e+03 4.064055e+03 3.773327e+03
    [49,] 1.161071e+04 5.064743e+03 4.702429e+03
    [50,] 1.446960e+04 6.311829e+03 5.860303e+03
    [51,] 1.803243e+04 7.865983e+03 7.303279e+03
    [52,] 2.247254e+04 9.802815e+03 9.101557e+03
    [53,] 2.800593e+04 1.221655e+04 1.134262e+04
    [54,] 3.490180e+04 1.522462e+04 1.413551e+04
    [55,] 4.349563e+04 1.897336e+04 1.761608e+04
    [56,] 5.420551e+04 2.364515e+04 2.195366e+04
    [57,] 6.755248e+04 2.946727e+04 2.735929e+04
    [58,] 8.418585e+04 3.672297e+04 3.409594e+04
    [59,] 1.049148e+05 4.576522e+04 4.249134e+04
    [60,] 1.307479e+05 5.703394e+04 5.295394e+04
    [61,] 1.629418e+05 7.107734e+04 6.599273e+04
    [62,] 2.030628e+05 8.857864e+04 8.224204e+04
    [63,] 2.530627e+05 1.103893e+05 1.024924e+05
    [64,] 3.153741e+05 1.375703e+05 1.277290e+05
    [65,] 3.930283e+05 1.714441e+05 1.591796e+05
    [66,] 4.898032e+05 2.136586e+05 1.983742e+05
    [67,] 6.104069e+05 2.662675e+05 2.472196e+05
    [68,] 7.607068e+05 3.318302e+05 3.080923e+05
    [69,] 9.480147e+05 4.135364e+05 3.839535e+05
    [70,] 1.181443e+06 5.153610e+05 4.784939e+05
    [71,] 1.472349e+06 6.422578e+05 5.963130e+05
    
    , , biasS2
    
     S1 S2 S3
     [1,] 0.000000e+00 1.000000e+00 0.000000e+00
     [2,] 1.000000e+00 1.000000e-01 6.000000e-01
     [3,] 1.300000e+00 5.100000e-01 4.200000e-01
     [4,] 1.350000e+00 7.010000e-01 5.580000e-01
     [5,] 1.817000e+00 7.451000e-01 7.554000e-01
     [6,] 2.255900e+00 9.830100e-01 9.003000e-01
     [7,] 2.783610e+00 1.226251e+00 1.129986e+00
     [8,] 3.486223e+00 1.514430e+00 1.413742e+00
     [9,] 4.341915e+00 1.894555e+00 1.756903e+00
    [10,] 5.408361e+00 2.360413e+00 2.190875e+00
    [11,] 6.742162e+00 2.940222e+00 2.730772e+00
    [12,] 8.401767e+00 3.665103e+00 3.402597e+00
    [13,] 1.047030e+01 4.567394e+00 4.240620e+00
    [14,] 1.304863e+01 5.691888e+00 5.284808e+00
    [15,] 1.626150e+01 7.093506e+00 6.586018e+00
    [16,] 2.026554e+01 8.840103e+00 8.207714e+00
    [17,] 2.525553e+01 1.101678e+01 1.022869e+01
    [18,] 3.147416e+01 1.372944e+01 1.274728e+01
    [19,] 3.922401e+01 1.711002e+01 1.588604e+01
    [20,] 4.888209e+01 2.132301e+01 1.979764e+01
    [21,] 6.091828e+01 2.657335e+01 2.467239e+01
    [22,] 7.591812e+01 3.311647e+01 3.074744e+01
    [23,] 9.461135e+01 4.127071e+01 3.831835e+01
    [24,] 1.179074e+02 5.143275e+01 4.775343e+01
    [25,] 1.469396e+02 6.409698e+01 5.951171e+01
    [26,] 1.831204e+02 7.987950e+01 7.416521e+01
    [27,] 2.282099e+02 9.954815e+01 9.242683e+01
    [28,] 2.844018e+02 1.240598e+02 1.151850e+02
    [29,] 3.544297e+02 1.546069e+02 1.435469e+02
    [30,] 4.417006e+02 1.926756e+02 1.788922e+02
    [31,] 5.504600e+02 2.401179e+02 2.229407e+02
    [32,] 6.859992e+02 2.992418e+02 2.778351e+02
    [33,] 8.549121e+02 3.729238e+02 3.462462e+02
    [34,] 1.065416e+03 4.647484e+02 4.315020e+02
    [35,] 1.327752e+03 5.791829e+02 5.377502e+02
    [36,] 1.654683e+03 7.217945e+02 6.701599e+02
    [37,] 2.062114e+03 8.995211e+02 8.351726e+02
    [38,] 2.569866e+03 1.121009e+03 1.040816e+03
    [39,] 3.202642e+03 1.397034e+03 1.297095e+03
    [40,] 3.991225e+03 1.741024e+03 1.616478e+03
    [41,] 4.973979e+03 2.169715e+03 2.014501e+03
    [42,] 6.198717e+03 2.703961e+03 2.510530e+03
    [43,] 7.725020e+03 3.369755e+03 3.128694e+03
    [44,] 9.627143e+03 4.199486e+03 3.899069e+03
    [45,] 1.199762e+04 5.233520e+03 4.859133e+03
    [46,] 1.495179e+04 6.522164e+03 6.055592e+03
    [47,] 1.863335e+04 8.128110e+03 7.546654e+03
    [48,] 2.322142e+04 1.012949e+04 9.404858e+03
    [49,] 2.893920e+04 1.262366e+04 1.172061e+04
    [50,] 3.606487e+04 1.573197e+04 1.460656e+04
    [51,] 4.494508e+04 1.960563e+04 1.820311e+04
    [52,] 5.601186e+04 2.443310e+04 2.268525e+04
    [53,] 6.980360e+04 3.044924e+04 2.827101e+04
    [54,] 8.699126e+04 3.794672e+04 3.523215e+04
    [55,] 1.084110e+05 4.729030e+04 4.390732e+04
    [56,] 1.351050e+05 5.893454e+04 5.471858e+04
    [57,] 1.683717e+05 7.344593e+04 6.819187e+04
    [58,] 2.098297e+05 9.153044e+04 8.498268e+04
    [59,] 2.614958e+05 1.140679e+05 1.059079e+05
    [60,] 3.258836e+05 1.421547e+05 1.319855e+05
    [61,] 4.061256e+05 1.771573e+05 1.644841e+05
    [62,] 5.061255e+05 2.207785e+05 2.049848e+05
    [63,] 6.307482e+05 2.751406e+05 2.554580e+05
    [64,] 7.860566e+05 3.428881e+05 3.183592e+05
    [65,] 9.796064e+05 4.273171e+05 3.967484e+05
    [66,] 1.220814e+06 5.325349e+05 4.944393e+05
    [67,] 1.521414e+06 6.636604e+05 6.161845e+05
    [68,] 1.896029e+06 8.270728e+05 7.679070e+05
    [69,] 2.362887e+06 1.030722e+06 9.569879e+05
    [70,] 2.944698e+06 1.284516e+06 1.192626e+06
    [71,] 3.669767e+06 1.600800e+06 1.486285e+06
    
    , , biasS3
    
     S1 S2 S3
     [1,] 0.000000e+00 0.000000e+00 1.000000e+00
     [2,] 2.000000e+00 0.000000e+00 6.000000e-01
     [3,] 1.200000e+00 1.000000e+00 3.600000e-01
     [4,] 1.720000e+00 7.000000e-01 8.160000e-01
     [5,] 2.332000e+00 9.300000e-01 9.096000e-01
     [6,] 2.749200e+00 1.259000e+00 1.103760e+00
     [7,] 3.466520e+00 1.500500e+00 1.417656e+00
     [8,] 4.335812e+00 1.883310e+00 1.750894e+00
     [9,] 5.385097e+00 2.356237e+00 2.180522e+00
    [10,] 6.717281e+00 2.928172e+00 2.722055e+00
    [11,] 8.372283e+00 3.651458e+00 3.390137e+00
    [12,] 1.043173e+01 4.551287e+00 4.224957e+00
    [13,] 1.300120e+01 5.670994e+00 5.265747e+00
    [14,] 1.620249e+01 7.067700e+00 6.562045e+00
    [15,] 2.019179e+01 8.808014e+00 8.177847e+00
    [16,] 2.516371e+01 1.097670e+01 1.019152e+01
    [17,] 3.135973e+01 1.367952e+01 1.270093e+01
    [18,] 3.908138e+01 1.704782e+01 1.582827e+01
    [19,] 4.870436e+01 2.124547e+01 1.972565e+01
    [20,] 6.069677e+01 2.647673e+01 2.458267e+01
    [21,] 7.564207e+01 3.299606e+01 3.063564e+01
    [22,] 9.426734e+01 4.112064e+01 3.817902e+01
    [23,] 1.174787e+02 5.124573e+01 4.757980e+01
    [24,] 1.464053e+02 6.386391e+01 5.929532e+01
    [25,] 1.824546e+02 7.958905e+01 7.389554e+01
    [26,] 2.273801e+02 9.918618e+01 9.209076e+01
    [27,] 2.833677e+02 1.236087e+02 1.147662e+02
    [28,] 3.531410e+02 1.540447e+02 1.430249e+02
    [29,] 4.400945e+02 1.919750e+02 1.782418e+02
    [30,] 5.484585e+02 2.392448e+02 2.221301e+02
    [31,] 6.835049e+02 2.981537e+02 2.768249e+02
    [32,] 8.518035e+02 3.715678e+02 3.449872e+02
    [33,] 1.061542e+03 4.630585e+02 4.299330e+02
    [34,] 1.322925e+03 5.770769e+02 5.357949e+02
    [35,] 1.648667e+03 7.191700e+02 6.677231e+02
    [36,] 2.054616e+03 8.962504e+02 8.321358e+02
    [37,] 2.560522e+03 1.116933e+03 1.037032e+03
    [38,] 3.190997e+03 1.391954e+03 1.292379e+03
    [39,] 3.976712e+03 1.734694e+03 1.610600e+03
    [40,] 4.955894e+03 2.161825e+03 2.007176e+03
    [41,] 6.176178e+03 2.694129e+03 2.501401e+03
    [42,] 7.696931e+03 3.357502e+03 3.117318e+03
    [43,] 9.592138e+03 4.184216e+03 3.884892e+03
    [44,] 1.195400e+04 5.214491e+03 4.841465e+03
    [45,] 1.489742e+04 6.498449e+03 6.033573e+03
    [46,] 1.856560e+04 8.098555e+03 7.519213e+03
    [47,] 2.313698e+04 1.009265e+04 9.370661e+03
    [48,] 2.883398e+04 1.257776e+04 1.167799e+04
    [49,] 3.593373e+04 1.567476e+04 1.455345e+04
    [50,] 4.478166e+04 1.953434e+04 1.813693e+04
    [51,] 5.580820e+04 2.434426e+04 2.260276e+04
    [52,] 6.954979e+04 3.033852e+04 2.816821e+04
    [53,] 8.667495e+04 3.780875e+04 3.510404e+04
    [54,] 1.080168e+05 4.711835e+04 4.374767e+04
    [55,] 1.346137e+05 5.872025e+04 5.451961e+04
    [56,] 1.677595e+05 7.317887e+04 6.794392e+04
    [57,] 2.090667e+05 9.119763e+04 8.467368e+04
    [58,] 2.605450e+05 1.136531e+05 1.055228e+05
    [59,] 3.246987e+05 1.416378e+05 1.315055e+05
    [60,] 4.046489e+05 1.765131e+05 1.638860e+05
    [61,] 5.042851e+05 2.199758e+05 2.042395e+05
    [62,] 6.284547e+05 2.741401e+05 2.545291e+05
    [63,] 7.831984e+05 3.416414e+05 3.172016e+05
    [64,] 9.760445e+05 4.257633e+05 3.953058e+05
    [65,] 1.216375e+06 5.305986e+05 4.926415e+05
    [66,] 1.515882e+06 6.612473e+05 6.139440e+05
    [67,] 1.889135e+06 8.240655e+05 7.651148e+05
    [68,] 2.354295e+06 1.026974e+06 9.535082e+05
    [69,] 2.933991e+06 1.279845e+06 1.188289e+06
    [70,] 3.656424e+06 1.594980e+06 1.480881e+06
    [71,] 4.556741e+06 1.987710e+06 1.845516e+06
    
    > bounds(pr) #bounds on population dynamics
     [,1] [,2]
     [1,] 1.000000e+00 1.000000e+00
     [2,] 5.000000e-01 2.600000e+00
     [3,] 8.500000e-01 2.560000e+00
     [4,] 1.115000e+00 3.236000e+00
     [5,] 1.304500e+00 4.171600e+00
     [6,] 1.658750e+00 5.111960e+00
     [7,] 2.069605e+00 6.384676e+00
     [8,] 2.569924e+00 7.970016e+00
     [9,] 3.207198e+00 9.921856e+00
    [10,] 3.996686e+00 1.236751e+01
    [11,] 4.979824e+00 1.541388e+01
    [12,] 6.206578e+00 1.920798e+01
    [13,] 7.734733e+00 2.393794e+01
    [14,] 9.639155e+00 2.983223e+01
    [15,] 1.201266e+01 3.717765e+01
    [16,] 1.497051e+01 4.633192e+01
    [17,] 1.865668e+01 5.774018e+01
    [18,] 2.325050e+01 7.195746e+01
    [19,] 2.897544e+01 8.967548e+01
    [20,] 3.611003e+01 1.117562e+02
    [21,] 4.500137e+01 1.392738e+02
    [22,] 5.608201e+01 1.735670e+02
    [23,] 6.989102e+01 2.163042e+02
    [24,] 8.710020e+01 2.695646e+02
    [25,] 1.085468e+02 3.359391e+02
    [26,] 1.352741e+02 4.186571e+02
    [27,] 1.685826e+02 5.217425e+02
    [28,] 2.100925e+02 6.502106e+02
    [29,] 2.618233e+02 8.103113e+02
    [30,] 3.262917e+02 1.009833e+03
    [31,] 4.066342e+02 1.258483e+03
    [32,] 5.067593e+02 1.568358e+03
    [33,] 6.315381e+02 1.954534e+03
    [34,] 7.870410e+02 2.435796e+03
    [35,] 9.808332e+02 3.035560e+03
    [36,] 1.222343e+03 3.783002e+03
    [37,] 1.523319e+03 4.714487e+03
    [38,] 1.898404e+03 5.875330e+03
    [39,] 2.365846e+03 7.322006e+03
    [40,] 2.948386e+03 9.124895e+03
    [41,] 3.674363e+03 1.137171e+04
    [42,] 4.579098e+03 1.417175e+04
    [43,] 5.706604e+03 1.766125e+04
    [44,] 7.111735e+03 2.200996e+04
    [45,] 8.862849e+03 2.742944e+04
    [46,] 1.104514e+04 3.418336e+04
    [47,] 1.376477e+04 4.260030e+04
    [48,] 1.715406e+04 5.308972e+04
    [49,] 2.137788e+04 6.616194e+04
    [50,] 2.664173e+04 8.245293e+04
    [51,] 3.320170e+04 1.027552e+05
    [52,] 4.137691e+04 1.280565e+05
    [53,] 5.156511e+04 1.595877e+05
    [54,] 6.426192e+04 1.988829e+05
    [55,] 8.008507e+04 2.478536e+05
    [56,] 9.980433e+04 3.088823e+05
    [57,] 1.243790e+05 3.849380e+05
    [58,] 1.550047e+05 4.797209e+05
    [59,] 1.931714e+05 5.978420e+05
    [60,] 2.407358e+05 7.450480e+05
    [61,] 3.000119e+05 9.285004e+05
    [62,] 3.738835e+05 1.157124e+06
    [63,] 4.659444e+05 1.442041e+06
    [64,] 5.806734e+05 1.797114e+06
    [65,] 7.236519e+05 2.239615e+06
    [66,] 9.018360e+05 2.791073e+06
    [67,] 1.123894e+06 3.478316e+06
    [68,] 1.400629e+06 4.334777e+06
    [69,] 1.745505e+06 5.402125e+06
    [70,] 2.175298e+06 6.732284e+06
    [71,] 2.710920e+06 8.389967e+06
    > mat(pr) #matrix used to create projection
     [,1] [,2] [,3]
    [1,] 0.0 1.0 2.0
    [2,] 0.5 0.1 0.0
    [3,] 0.0 0.6 0.6
    > Aseq(pr) #sequence of matrices (more useful for stochastic projections)
     [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
    [39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
    > projtype(pr) #type of projection
    [1] "deterministic"
    > vectype(pr) #type of vector(s) initiating projection
    [1] "bias"
    >
    > # Extra information on the projection
    > nproj(pr) #number of projections
    [1] 3
    > nmat(pr) #number of matrices (more usefulk for stochastic projections)
    [1] 1
    > ntime(pr) #number of time intervals
    [1] 70
    >
    > # Select the projection of stage 2 bias
    > pr[,2]
     [1] 1.000000e+00 1.700000e+00 2.230000e+00 2.609000e+00 3.317500e+00
     [6] 4.139210e+00 5.139847e+00 6.414395e+00 7.993372e+00 9.959649e+00
    [11] 1.241316e+01 1.546947e+01 1.927831e+01 2.402533e+01 2.994103e+01
    [16] 3.731336e+01 4.650100e+01 5.795089e+01 7.222007e+01 9.000274e+01
    [21] 1.121640e+02 1.397820e+02 1.742004e+02 2.170936e+02 2.705483e+02
    [26] 3.371651e+02 4.201849e+02 5.236466e+02 6.525835e+02 8.132684e+02
    [31] 1.013519e+03 1.263076e+03 1.574082e+03 1.961666e+03 2.444685e+03
    [36] 3.046638e+03 3.796808e+03 4.731692e+03 5.896771e+03 7.348726e+03
    [41] 9.158195e+03 1.141321e+04 1.422347e+04 1.772570e+04 2.209028e+04
    [46] 2.752954e+04 3.430811e+04 4.275576e+04 5.328346e+04 6.640339e+04
    [51] 8.275383e+04 1.031302e+05 1.285238e+05 1.601701e+05 1.996087e+05
    [56] 2.487581e+05 3.100095e+05 3.863428e+05 4.814716e+05 6.000238e+05
    [61] 7.477670e+05 9.318888e+05 1.161347e+06 1.447304e+06 1.803672e+06
    [66] 2.247788e+06 2.801258e+06 3.491009e+06 4.350597e+06 5.421839e+06
    [71] 6.756853e+06
    >
    > # Project stage-biased dynamics of standardised A over 30 intervals
    > ( pr2 <- project(A, vector="n", time=30, standard.A=TRUE) )
    3 deterministic population projections (stage-biased initial vectors) over 30 time intervals.
    
     biasS1 biasS2 biasS3
     [1,] 1.0000000 1.000000 1.000000
     [2,] 0.4012104 1.364115 2.086294
     [3,] 0.5472973 1.435851 1.648331
     [4,] 0.5760782 1.347971 1.671918
     [5,] 0.5408201 1.375370 1.729463
     [6,] 0.5518129 1.376982 1.700585
     [7,] 0.5524596 1.372029 1.704323
     [8,] 0.5504723 1.373950 1.707161
     [9,] 0.5512431 1.373876 1.705338
    [10,] 0.5512133 1.373611 1.705697
    [11,] 0.5511069 1.373738 1.705822
    [12,] 0.5511581 1.373724 1.705712
    [13,] 0.5511522 1.373710 1.705741
    [14,] 0.5511468 1.373718 1.705745
    [15,] 0.5511501 1.373717 1.705739
    [16,] 0.5511495 1.373716 1.705741
    [17,] 0.5511493 1.373717 1.705741
    [18,] 0.5511495 1.373717 1.705740
    [19,] 0.5511494 1.373717 1.705741
    [20,] 0.5511494 1.373717 1.705741
    [21,] 0.5511494 1.373717 1.705741
    [22,] 0.5511494 1.373717 1.705741
    [23,] 0.5511494 1.373717 1.705741
    [24,] 0.5511494 1.373717 1.705741
    [25,] 0.5511494 1.373717 1.705741
    [26,] 0.5511494 1.373717 1.705741
    [27,] 0.5511494 1.373717 1.705741
    [28,] 0.5511494 1.373717 1.705741
    [29,] 0.5511494 1.373717 1.705741
    [30,] 0.5511494 1.373717 1.705741
    [31,] 0.5511494 1.373717 1.705741
    > plot(pr2)
    >
    > #Select the projection of stage 2 bias
    > pr2[,2]
     [1] 1.000000 1.364115 1.435851 1.347971 1.375370 1.376982 1.372029 1.373950
     [9] 1.373876 1.373611 1.373738 1.373724 1.373710 1.373718 1.373717 1.373716
    [17] 1.373717 1.373717 1.373717 1.373717 1.373717 1.373717 1.373717 1.373717
    [25] 1.373717 1.373717 1.373717 1.373717 1.373717 1.373717 1.373717
    >
    > # Select the density of stage 3 in bias 2 at time 10
    > vec(pr2)[11,3,2]
    [1] 0.302209
    >
    > # Select the time series of densities of stage 2 in bias 1
    > vec(pr2)[,2,1]
     [1] 0.00000000 0.40121040 0.03219396 0.13174882 0.14531042 0.12393543
     [7] 0.13120217 0.13133021 0.13014776 0.13064619 0.13061100 0.13054917
    [13] 0.13058172 0.13057704 0.13057405 0.13057608 0.13057566 0.13057553
    [19] 0.13057565 0.13057562 0.13057561 0.13057562 0.13057562 0.13057562
    [25] 0.13057562 0.13057562 0.13057562 0.13057562 0.13057562 0.13057562
    [31] 0.13057562
    >
    > #Select the matrix of population vectors for bias 2
    > vec(pr2)[,,2]
     S1 S2 S3
     [1,] 0.0000000 1.00000000 0.0000000
     [2,] 0.8024208 0.08024208 0.4814525
     [3,] 0.8370429 0.32837837 0.2704292
     [4,] 0.6974937 0.36218008 0.2882974
     [5,] 0.7532925 0.30890383 0.3131740
     [6,] 0.7504655 0.32701588 0.2995009
     [7,] 0.7430559 0.32733501 0.3016381
     [8,] 0.7467418 0.32438779 0.3028207
     [9,] 0.7462747 0.32563011 0.3019711
    [10,] 0.7459081 0.32554241 0.3021601
    [11,] 0.7461412 0.32538829 0.3022090
    [12,] 0.7460958 0.32546944 0.3021583
    [13,] 0.7460796 0.32545776 0.3021729
    [14,] 0.7460937 0.32545030 0.3021743
    [15,] 0.7460901 0.32545538 0.3021714
    [16,] 0.7460895 0.32545431 0.3021725
    [17,] 0.7460903 0.32545399 0.3021725
    [18,] 0.7460900 0.32545430 0.3021723
    [19,] 0.7460900 0.32545421 0.3021724
    [20,] 0.7460901 0.32545420 0.3021724
    [21,] 0.7460901 0.32545422 0.3021724
    [22,] 0.7460901 0.32545421 0.3021724
    [23,] 0.7460901 0.32545421 0.3021724
    [24,] 0.7460901 0.32545421 0.3021724
    [25,] 0.7460901 0.32545421 0.3021724
    [26,] 0.7460901 0.32545421 0.3021724
    [27,] 0.7460901 0.32545421 0.3021724
    [28,] 0.7460901 0.32545421 0.3021724
    [29,] 0.7460901 0.32545421 0.3021724
    [30,] 0.7460901 0.32545421 0.3021724
    [31,] 0.7460901 0.32545421 0.3021724
    >
    > # Create an initial stage structure
    > ( initial <- c(1,3,2) )
    [1] 1 3 2
    >
    > # Project A over 50 intervals using a specified population structure
    > ( pr3 <- project(A, vector=initial, time=50) )
    1 deterministic population projection over 50 time intervals.
    
     [1] 6.00000 10.80000 12.66000 15.41400 19.60020
     [6] 24.30030 30.25850 37.75314 47.03103 58.61065
    [11] 73.04705 91.03093 113.44555 141.37961 176.19104
    [16] 219.57442 273.64004 341.01809 424.98660 529.63059
    [21] 660.04094 822.56210 1025.10066 1277.51007 1592.06998
    [26] 1984.08361 2472.62234 3081.45342 3840.19631 4785.76363
    [31] 5964.15694 7432.70475 9262.85146 11543.63320 14386.00931
    [36] 17928.26056 22342.71644 27844.13895 34700.17067 43244.35554
    [41] 53892.36565 67162.22360 83699.50407 104308.74092 129992.56751
    [46] 162000.49449 201889.69812 251600.77652 313552.15908 390757.76246
    [51] 486973.61667
    > plot(pr3)
    >
    > # Project standardised dynamics of A over 10 intervals using
    > # standardised initial structure and return demographic vectors
    > ( pr4 <- project(A, vector=initial, time=10, standard.vec=TRUE,
    + standard.A=TRUE, return.vec=TRUE) )
    1 deterministic population projection over 10 time intervals.
    
     [1] 1.000000 1.444357 1.358585 1.327305 1.354310 1.347322 1.346199 1.347774
     [9] 1.347258 1.347240 1.347328
    > plot(pr4)
    >
    > # Select the time series for stage 1
    > vec(pr4)[,1]
     [1] 0.1666667 0.9361576 0.7297297 0.7009382 0.7455537 0.7304611 0.7301609
     [8] 0.7326318 0.7315953 0.7316666 0.7317960
    >
    > ### DETERMINISTIC PROJECTIONS
    >
    > # Load the desert Tortoise matrix
    > data(Tort)
    >
    > # Create an initial stage structure
    > Tortvec1 <- c(8, 7, 6, 5, 4, 3, 2, 1)
    >
    > # Create a projection over 30 time intervals
    > ( Tortp1 <- project(Tort, vector = Tortvec1, time = 10) )
    1 deterministic population projection over 10 time intervals.
    
     [1] 36.00000 38.17600 40.12405 41.64962 42.63685 43.03815 42.86468 42.17663
     [9] 41.06748 39.64640 38.02259
    >
    > # plot p1
    > plot(Tortp1)
    > plot(Tortp1, bounds = TRUE) #with bounds
    >
    > # new display parameters
    > plot(Tortp1, bounds = TRUE, col = "red", bty = "n", log = "y",
    + ylab = "Number of individuals (log scale)",
    + bounds.args = list(lty = 2, lwd = 2) )
    >
    > # multiple vectors
    > Tortvec2 <- cbind(Tortvec1, c(1, 2, 3, 4, 5, 6, 7, 8))
    > plot(project(Tort, vector = Tortvec2), log = "y")
    > plot(project(Tort, vector = Tortvec2), log = "y", labs = FALSE) #no labels
    >
    > # dirichlet distribution
    > # darker shading indicates more likely population size
    > plot(project(Tort, time = 30, vector = "diri", standard.A = TRUE,
    + draws = 500, alpha.draws = "unif"),
    + plottype = "shady", bounds = TRUE)
    Error in (function (classes, fdef, mtable) :
     unable to find an inherited method for function 'plot' for signature '"Projection"'
    Calls: plot -> <Anonymous>
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64