Error(s) in re-building vignettes:
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--- re-building ‘example.Rmd’ using rmarkdown
Warning in engine$weave(file, quiet = quiet, encoding = enc) :
  Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.5-1 (2019-12-12) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

varycoef               package:varycoef                R Documentation

_v_a_r_y_c_o_e_f: _M_o_d_e_l_i_n_g _S_p_a_t_i_a_l_l_y _V_a_r_y_i_n_g _C_o_e_f_f_i_c_i_e_n_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     This package offers functions to estimate and predict spatially
     varying coefficient (SVC) models. Briefly described, one
     generalizes a linear regression equation such that the
     coefficients are no longer constant, but have the possibility to
     vary spatially. This is enabled by modelling the coefficients by
     Gaussian processes with (currently) either an exponential or
     spherical covariance function. The advantages of such SVC models
     are that they are usually quite easy to interpret, yet they offer
     a very highe level of flexibility.

_D_e_t_a_i_l_s:

     The underlying methodology is decribed in Dambon et al. (2020)
     <URL: https://arxiv.org/abs/2001.08089>, where further details can
     be found.

_E_s_t_i_m_a_t_i_o_n _a_n_d _P_r_e_d_i_c_t_i_o_n:

     The ensemble of the function 'SVC_mle' and the method 'predict'
     estimates the defined SVC model and gives predictions of the SVC
     as well as the response for some pre-defined locations. This
     concept should be rather familiar as it is the same for the
     classical regression ('lm') or local polynomial regression
     ('loess'), to name a couple. As the name suggests, we are using a
     MLE approach in order to estimate the model and following the
     empirical best linear unbiased predictor to give location-specifc
     predictions. A detailed tutorial with examples is given in a
     vignette; call 'vignette("example", package = "varycoef")'.

_M_e_t_h_o_d_s:

     With the before mentioned 'SVC_mle' function one gets an object of
     class 'SVC_mle'. And like the method 'predict' for predictions,
     there are several more methods in order to diagnose the model, see
     'methods(class = "SVC_mle")'.

_A_u_t_h_o_r(_s):

     Jakob Dambon

_E_x_a_m_p_l_e_s:

     vignette("manual", package = "varycoef")
     methods(class = "SVC_mle")
     

New output format of RFsimulate: S4 object of class 'RFsp';
for a bare, but faster array format use 'RFoptions(spConform=FALSE)'.
meuse                    package:sp                    R Documentation

_M_e_u_s_e _r_i_v_e_r _d_a_t_a _s_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     This data set gives locations and topsoil heavy metal
     concentrations, along with a number of soil and landscape
     variables at the observation locations, collected in a flood plain
     of the river Meuse, near the village of Stein (NL). Heavy metal
     concentrations are from composite samples of an area of
     approximately 15 m x 15 m.

_U_s_a_g_e:

     data(meuse)
     
_F_o_r_m_a_t:

     This data frame contains the following columns:

     x a numeric vector; Easting (m) in Rijksdriehoek (RDH)
          (Netherlands topographical) map coordinates

     y a numeric vector; Northing (m) in RDH coordinates

     cadmium topsoil cadmium concentration, mg kg-1 soil ("ppm"); zero
          cadmium values in the original data set have been shifted to
          0.2 (half the lowest non-zero value)

     copper topsoil copper concentration, mg kg-1 soil ("ppm")

     lead topsoil lead concentration, mg kg-1 soil ("ppm")

     zinc topsoil zinc concentration, mg kg-1 soil ("ppm")

     elev relative elevation above local river bed, m

     dist distance to the Meuse; obtained from the nearest cell in
          meuse.grid, which in turn was derived by a spread (spatial
          distance) GIS operation, horizontal precision 20 metres; then
          normalized to $[0,1]$

     om organic matter, kg (100 kg)-1 soil (percent)

     ffreq flooding frequency class: 1 = once in two years; 2 = once in
          ten years; 3 = one in 50 years

     soil soil type according to the 1:50 000 soil map of the
          Netherlands. 1 = Rd10A (Calcareous weakly-developed meadow
          soils, light sandy clay); 2 = Rd90C/VII (Non-calcareous
          weakly-developed meadow soils, heavy sandy clay to light
          clay); 3 = Bkd26/VII (Red Brick soil, fine-sandy, silty light
          clay)

     lime lime class: 0 = absent, 1 = present by field test with 5% HCl

     landuse landuse class: Aa Agriculture/unspecified = , Ab =
          Agr/sugar beetsm, Ag = Agr/small grains, Ah = Agr/??, Am =
          Agr/maize, B = woods, Bw = trees in pasture, DEN = ??, Fh =
          tall fruit trees, Fl = low fruit trees; Fw = fruit trees in
          pasture, Ga = home gardens, SPO = sport field, STA = stable
          yard, Tv = ?? , W = pasture

     dist.m distance to river Meuse in metres, as obtained during the
          field survey

_N_o_t_e:

     row.names refer to the original sample number.

     Soil units were mapped with a minimum delination width of 150 m,
     and so somewhat generalize the landscape.

     Approximate equivalent World Reference Base 2002 for Soil
     Resources names are: Rd10A Gleyic Fluvisols; Rd90C Haplic
     Fluvisols; Bkd26 Haplic Luvisols. Units Rd90C and Bkd26 have
     winter groundwater > 80cm, summer > 120cm depth.

_A_u_t_h_o_r(_s):

     Field data were collected by Ruud van Rijn and Mathieu Rikken;
     compiled for R by Edzer Pebesma; description extended by David
     Rossiter

_R_e_f_e_r_e_n_c_e_s:

     M G J Rikken and R P G Van Rijn, 1993. Soil pollution with heavy
     metals - an inquiry into spatial variation, cost of mapping and
     the risk evaluation of copper, cadmium, lead and zinc in the
     floodplains of the Meuse west of Stein, the Netherlands.
     Doctoraalveldwerkverslag, Dept. of Physical Geography, Utrecht
     University

     P.A. Burrough, R.A. McDonnell, 1998. Principles of Geographical
     Information Systems. Oxford University Press.

     Stichting voor Bodemkartering (STIBOKA), 1970. Bodemkaart van
     Nederland : Blad 59 Peer, Blad 60 West en 60 Oost Sittard: schaal
     1 : 50 000. Wageningen, STIBOKA.

     <URL: http://www.gstat.org/>

_E_x_a_m_p_l_e_s:

     data(meuse)
     summary(meuse)
     coordinates(meuse) <- ~x+y
     proj4string(meuse) <- CRS("+init=epsg:28992")
     

Variable(s) "l_cad" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
Linking to GEOS 3.6.3, GDAL 2.2.4, PROJ 5.2.0
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Error: processing vignette 'example.Rmd' failed with diagnostics:
there is no package called 'webshot'
--- failed re-building ‘example.Rmd’

--- re-building ‘manual.Rmd’ using rmarkdown
Warning in engine$weave(file, quiet = quiet, encoding = enc) :
  Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
New output format of RFsimulate: S4 object of class 'RFsp';
for a bare, but faster array format use 'RFoptions(spConform=FALSE)'.
--- finished re-building ‘manual.Rmd’

SUMMARY: processing the following file failed:
  ‘example.Rmd’

Error: Vignette re-building failed.
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