NEWS.md

In forestgeo/ctfs: Tools for the Analysis of Forest Dynamics

ctfs 0.0.0.9001

• Removed assertive and ggplot2 because they were not used
• Move some packages from suggest to imports, because functions need them
• Replace data in bci with data in bciex.

forestr 0.0.0.9000

2017-06-05 (week starting on)

DONE

• Deprecate is.leap(); recommend lubridate::leap_year().

• Refer to BCI data using new names, replacing old names (e.g. bci.full1 by bci::bci12full1).

CONVENTIONS

New sections:

Reuse good code, and Alternative

To direct users to better alternatives:

via @template reuse_good_code, add:

"The single biggest way to improve both the quality of your code and your productivity is to reuse good code (...) Many algorithms have already been invented, tested, discussed in the trade literature, reviewed, and improved".

--Code complete 2, by Steve McConnell.

Then, show a better alternative via section __Alternative_. For examples see left(), convert.factor().

Arguments details

Gives details of arguments that are specific for the current function, and may better be kept separate to what is documented more generally via @param, which can be reused via @template or @inheritParams).

New tags:

Find these tags with shift + f

SOURCE CODE

• Unused. To tag unused arguments. Search this tag with shift + ctrl + F.

• xxx_amend_source To tag source code that needs work.

DOCUMENTATION

• xxx_amend_doc To tag documentation that I need to amend later. Find these tags with shift + f.

• xxxdocparam To tag arguments that need to be documented. They are discoverable with find_xxxdocparam()

XXX END OF NOT REPORTED WORK

Improved arguments documentation

Documentation is one of the most important aspects of a good package. Without it, users won’t know how to use your package. Documentation is also useful for future-you (so you remember what your functions were supposed to do), and for developers extending your package.

-- http://r-pkgs.had.co.nz/man.html

Documentation of forestr has two large problems: the arguments of many function are undocumented, and many arguments that mean the same accross multiple functions are documented in multiple places. Those problems can in part be solved by re-using arguments shared across multiple functions, when the arguments are documented in one but not all of those functions. This also reduces duplication, which makes easier to maintain documentation (see Inheriting parameters from other functions, by Hadley Wickham).

Where to start from?

• (Comes after item below), identified what arguments should be documented with highest priority, to maximize the positive impact by unit of effort. I wrote functions to identify what arguments are most commonly used in forestr functions, but less commonly documented.

Example 1: The list below shows that the argument x is used 130 times (by functions including border.distance, cartesian.to.polar, etc.), but x is only documented 8 times.

> args_count_formals_man()
# A tibble: 1,758 x 4
   params                 fun frml_n man_n
    <chr>               <chr>  <int> <int>
 1      x     border.distance    130     8
 2      x  cartesian.to.polar    130     8
 3      x coldata.to.imagemat    130     8
 4      x         fullellipse    130     8
 5      x      getquadratname    130     8
 6      x     graphFilledBand    130     8
 7      x        plot_wavelet    130     8
 8      x            tojulian    130     8
 9      x      abundmodel.fit    130    NA
10      x         addBinParam    130    NA
# ... with 1,748 more rows

Note: unfortunately, x is used with very different meaning in different functions, so it is not a great candidate to improve documentation fast. Other arguments are better candidates.

Example 2: The argument plotdim is a good candidate to improve documentation fast. It is needed by 37 functions but is documented in only 29. Its meaning is very consistent accross functions, so it can be documented in one place via \@templates and then re-used from the functions that need it (they all point to the same tamplate).

args_count_formals_man() %>% 
  filter(params == "plotdim") 
# A tibble: 37 x 4
    params               fun frml_n man_n
     <chr>             <chr>  <int> <int>
 1 plotdim  abundanceperquad     37    29
 2 plotdim  allquadratslopes     37    29
 3 plotdim            Annuli     37    29
 4 plotdim   border.distance     37    29
 5 plotdim      CalcRingArea     37    29
 6 plotdim  complete.plotmap     37    29
 7 plotdim     coverage.diag     37    29
 8 plotdim          distance     37    29
 9 plotdim   findborderquads     37    29
10 plotdim findneighborabund     37    29
# ... with 27 more rows

Reduced duplicated instances of arguments documentation

• Reduced duplicated instances of arguments documentation from 132 to 41, mostly via roxygen2 @templates and @inheritParams.

BEFORE: 43 arguments were repeated multiple times (see n in table below), adding up to 132 duplicated instances.

             params     n
              <chr> <int>
 0          plotdim     9
 1         gridsize     8
 2           mindbh     6
 3                x     6
 4         censdata     5
 5            debug     5
 6             ycol     5
 7                r     4
 8             type     4
 9           xrange     4
10           yrange     4
11             data     3
12           export     3
13          outfile     3
14         showstep     3
15             size     3
16            steps     3
17             xcol     3
18                y     3
19              ...     2
20         badparam     2
21       badSDparam     2
22           burnin     2
23             clrs     2
24            error     2
25         filepath     2
26          graphit     2
27               ht     2
28            ltype     2
29           lwidth     2
30            model     2
31        modeltype     2
32             path     2
33           ptsize     2
34           sdfunc     2
35            start     2
36          startSD     2
37 subquadratsuffix     2
38           update     2
39               wd     2
40            xname     2
41            yname     2
42                z     2
-------------------------
sum(n)                132

AFTER: 18 arguments remain that have the same name in two or more functions, adding up to 41 instances. These instances of arguments with thae same name either mean different things in different fuctions or, rarely, if they mean the same or not is unclear.

    params     n
     <chr> <int>
 1    type     4
 2       x     4
 3       y     3
 4    clrs     2
 5    data     2
 6 graphit     2
 7       h     2
 8    path     2
 9  ptsize     2
10       r     2
11  sdfunc     2
12 startSD     2
13       w     2
14    xcol     2
15  xrange     2
16    ycol     2
17  yrange     2
18       z     2
----------------
sum(n)        41

Documented arguments which functions names are similar in CTFS-CRAN

• Documented arguments from functions of forestr which function names are similar to functions from CTFS-CRAN. After working on the item below, I documented functions which name is not exactly the same but similar, suggesting that those functions from forestr and CTFS-CRAN have a lot in common and therefore the information from CTFS-CRAN helped fill a few more gaps in the documentation of forestr.

Documented arguments which functions names match exactly function names in CTFS-CRAN

• Documented arguments by reviewing only ~30 functions from forestr which name is exactly the same as in CTFS-CRAN (see list below). This match helped document arguments which meaning was unclear from only one source of information. Undocumented arguments reduced by approx. 120 (from 1,150 to 1,036).

Those functions are these:

 [1] "abundance.Rd"            
 [2] "abundance.spp.Rd"        
 [3] "assemble.demography.Rd"  
 [4] "ba.Rd"                   
 [5] "biomass.change.Rd"       
 [6] "elev.to.list.Rd"         
 [7] "find.climits.Rd"         
 [8] "findborderquads.Rd"      
 [9] "growth.dbh.Rd"           
[10] "growth.eachspp.Rd"       
[11] "growth.indiv.Rd"         
[12] "growth.Rd"               
[13] "gxgy.to.hectindex.Rd"    
[14] "gxgy.to.index.Rd"        
[15] "gxgy.to.rowcol.Rd"       
[16] "index.to.gxgy.Rd"        
[17] "index.to.rowcol.Rd"      
[18] "map.Rd"                  
[19] "maptopo.Rd"              
[20] "mortality.calculation.Rd"
[21] "mortality.dbh.Rd"        
[22] "mortality.eachspp.Rd"    
[23] "mortality.Rd"            
[24] "readelevdata.Rd"         
[25] "recruitment.eachspp.Rd"  
[26] "recruitment.Rd"          
[27] "rowcol.to.index.Rd"      
[28] "tojulian.Rd"             
[29] "trim.growth.Rd"      

Notes

• Although the 29 functions above have the same name, some differ in the source code, including arguments number, name, and definition.

• There are approximately 377 functions in forestr versus only 88 in CTFS-CRAN.

• Some arguments remain undocumented, and they can be found with the internal function find_xxxdocparam():

> find_xxxdocparam()
[1] "#' @param cens1,cens2 xxxdocparam"
[2] "#' @param time xxxdocparam"       
[3] "#' @param xaxis xxxdocparam"      
[4] "#' @param yaxis xxxdocparam"      
[5] "#' @param labelsize xxxdocparam"  
[6] "#' @param clr xxxdocparam"        
[7] "#' @param classbreak xxxdocparam" 
[8] "#' @param meantime xxxdocparam"  

I tried to use documentation in forestr exclusively and used documentation from CTFS-CRAN only to fill gaps (most often missing parameters' documentation) and as a source of information to rewrite forestr's documentation. Some limitations in merging documentation in forestr and CTFS-CRAN are:

Future improvements

Further improving documentation is much easier now than before. For any function, its arguments documentation can now be explored with args_explore("function_name"), for example:

> args_explore("growth")
All arguments are documented somewhere.
$args_of
# A tibble: 12 x 1
   arguments
       <chr>
 1   census1
 2   census2
 3 rounddown
 4    method
 5     stdev
 6   dbhunit
 7    mindbh
 8 growthcol
 9 err.limit
10   maxgrow
11    split1
12    split2

$args_by_fun
# A tibble: 17 x 3
                   fun    params                               definition
                 <chr>     <chr>                                    <chr>
 1      biomass.change   census1 The R Analytical Table for a single c...
 2      biomass.change   census2 The matching R Analytical Table for a...
 3           abundance   dbhunit 'cm' or 'mm', only used for basal are...
 4         trim.growth err.limit A number. Numbers such as 10000 are h...
 5         trim.growth   maxgrow A number. Numbers such as 10000 are h...
 6              growth growthcol defines how growth is measured, eithe...
 7              growth    method Use 'I' to calculate annual dbh incre...
 8           abundance    mindbh the minimum diameter above which the ...
 9    complete.plotmap    mindbh         "smallest dbh to include\r\n#' "
10 model.littleR.Gibbs    mindbh minimum dbh to be included; all trees...
11   NeighborDensities    mindbh (10) minimum size of neighbors to be ...
12              RipUvK    mindbh the minimum dbh to include in results...
13          spparea.sq    mindbh        "the minimum dbh included\r\n#' "
14              growth rounddown If TRUE, all dbh < 55 are rounded dow...
15           abundance    split1 a vector of categories, one per indiv...
16           abundance    split2 another vector of categories, one per...
17              growth     stdev Logical. Default (FALSE) returns conf...

$args_undoc
NULL

• To see all documented parameters use the internal function table_params_all() or the internal object params_table (see it online).

Enhanced

• To help users find the functions they need the online functions' reference now gives more information about each function and better organized. Using the folder and file name where each function lived in the CTFSRPackage, I arranged the functions alphabetically by the name of folder, file and function. And I extracted text from the description of each function to give some indication of what each function does. I did all this pragmatically; it would improve with human curation.

• Clone from ctfs 0.0.0.9005 (advice)

ctfs 0.0.0.9005

Fixed

• Fixed name of functions that where automatically interpreted as S3 methods

To permanently fix this problem, I replaced dots "." by "_" in the names of those problematic functions. The change should be easy to digest by users. The new names of those functions shows up with RStudio's auto-completion, and the change has been detailed in the documentation of each function.

The functions are these:

1     density.ind  # replaced by density_ind
2        exp.2par  # replaced by exp_2par
3    hist.compare  # replaced by hist_compare
4 image.dataframe  # replaced by image_dataframe
5       log.model  # replaced by log_model
6      merge.data  # replaced by merge_data
7    plot.wavelet  # replaced by plot_wavelet
8      solve.topo  # replaced by solve_topo
9      split.data  # replaced by split_data

• Fixed NOTE on partially matched arguments.

complete.plotmap: warning ...: partial argument match of 'exp' to 'export'

dgammaMinusdexp: warning in pgamma(z, shape = a, rate = r + lambda,
  lower.tail = FALSE, log = TRUE): partial argument match of 'log' to
  'log.p'

map: warning in define.graphwindow(exp = export, h = ht, w = wd, file =
  filename): partial argument match of 'exp' to 'export'

map: warning in maptopo(elev = elevdata, plotdim = plotdim, xaxis =
  xaxis, yaxis = yaxis, interval = topoint, ht = ht, wd = wd, plotside
  = plotside, labelsize = labsize, axspos = axspos, bgcolor = bgcolor,
  clr = topoclr): partial argument match of 'elev' to 'elevmat'
run.growthbin.manyspp: warning in run.growthfit.bin(growthdata =
  spdata, size = size, binoption = binoption, startpar = startpar,
  sdmodel = sdmodel, startsdpar = startsdpar, badsdfunc = badsdfunc,
  norep = noreps, noburn = noburn, noshow = noshow, ...): partial
  argument match of 'norep' to 'noreps'

selectrandomquad2: warning in index.to.gxgy(r, grid = 1, plotdim =
  plotdim - size): partial argument match of 'grid' to 'gridsize'

• Fixed one problem in wavelet.univariate(), which erroneously referred to wavelet.univariate() as wavelet.var(). Also fixed an erroneous reference to wavelet.var() in examples of wavelet.univariate().

• Fixed bug in model.littleR.Gibbs(). In a code chunk, lowercase names of the data set passed to the argument sptable because the variable idlevel was referred to with inconsistent case. After that chunk the original names were recovered to avoid potential problems downstream.

• Fixed bugs in pdf.allplot() and png.allplot() by replacing subset() by [ in map(). subset() should not be used inside functions.

• Fixed wavelet.bivariate(), replace as.real (defunc) by as.double. Detect missing argument type and wrote a warning in the help file.

Enhanced

• Updated persons' roles

The version of CTFS archived on CRAN contained the following information (file AUTHORS):

Authors to date:
Rick Condit <[e-mail]>
Pamela Hall <[e-mail]>
Suzanne Lao  <[e-mail]>
Kyle Harms  <[e-mail]>
Akira Itoh  <[e-mail]>

Conservatively, every person listed above is described as authors in the current development (if this needs to change let me know):

Authors@R: c(
    person("CTFS-ForestGEO", , , "[e-mail]", role = c("cph", "fnd")),
    person("Rick", "Condit", , "[e-mail]", role = c("aut")),
    person("Pamela", "Hall", , "[e-mail]", role = c("aut")),
    person("Suzanne", "Lao", , "[e-mail]", role = c("aut")),
    person("Kyle", "Harms", , "[e-mail]", role = c("aut")),
    person("Mauro", "Lepore", , "[e-mail]", role = c("aut", "ctr", "cre"))
    )

Persons responsibilities are listed in ?person. Some useful ones are these:

"aut": (Author) Use for full authors who have made substantial contributions to the package and should show up in the package citation.

"ctb": (Contributor) Use for authors who have made smaller contributions (such as code patches etc.) but should not show up in the package citation.

"cph": (Copyright holder) Use for all copyright holders.

"cre": (Creator) Use for the package maintainer.

"ctr": (Contractor) Use for authors who have been contracted to write (parts of) the package and hence do not own intellectual property.

• Refer to functions from external packages explicitly with pkg::fun()

  • mvtnorm::dmvnorm and mvtnorm::rmvnorm, used in

    • graph.mvnorm()
    • lmerBayes.hyperllike.sigma()
    • lmerBayes.hyperllike.mean()
    • llike.model.lmer()

  • MCMCpack::rinvgamma, used in:

    • Gibbs.regsigma()
    • Gibbs.normalvar()

  • splancs::spoints, used in: -NeighborDensities() -NDcount() -RipUvK()

  • splancs::inpip, used in: NeighborDensities()

  • splancs::dsquare, used in:

    • NeighborDensities()

  • date::mdy.date and date::date.mmddyy, used in:

    • tojulian()
    • fromjulian()
    • mortality.eachspp()

...this is what I recommend: list the package in DESCRIPTION so that it’s installed, then always refer to it explicitly with pkg::fun(). Unless there is a strong reason not to, it’s better to be explicit.

--R packages, by Hadley Wickham

• Enhanced source code of functions listed below to defensively avoid non standard evaluation (not appropriate for programming). Replaced subset() by [ in:

  • model.littleR.Gibbs(),
  • density.ind(),
  • abund.manycensus()
  • individual_grow.table()
  • individual_mort.table()
  • extract.growthdata()
  • graph.outliers.spp()
  • lmerBayes()
  • complete.plotmap()
  • modelBayes(): but errs. It also erred before replacing subset() by [
  • NeighborDensities()
  • allquadratslopes()
  • spparea.sq()
  • split.data()
  • countspp()

• Enhanced documentation of functions listed below by introducing ?wsgdata_dummy(), a function to create dummy wood density tables.

  • biomass.CTFSdb()
  • density.ind()

• In individual_grow.table()

  • enhanced documentation: @return, @param rnd, @param cnsdata
  • enhanced source code: remove default of data argument cnsdata

• Suggest package bci, doesn't import it because bci is in a private repo, so the user needs to provide a private token.

• Enhanced documentation in wavelet.bivariate(): argument type is missing from function definition, so I added a warning.

Deprecated

• attach_if_needed(): "In programming, functions should not change the search path unless that is their purpose" (Good practice in ?attach).

• CTFSplot(): no longer necessary because bci data is now available via the bci package.

Notes

• Added tests to verify that functions amended output equal values before and after the fix.

• Added a new vignette to test plots output remain the same after fixes to plotting functions. Follows advice in Testing R Code, by Richard Cotton.

• Added a new vignette to report package quality

• Added a new utility functions to remove rows full of NAs from data frames and matrices. Exported to access it from other functions easily, but removed from index with @keywords internal.

• Created a private repo on forestgeo to store the CTFS version archived on CRAN, which seems to be a useful source for missing documentation and data, and for deciding what are the most important functions.

• Tested all functions used in tutorials and out of a total of 28 functions, 13 failed to run. In addition to errors, I detected a number of other problems, described next.

• In website references, grouped functions by file. This will later improve to include original source folder and the first line of the description of each function.

To fix

• Document undocumented arguments

The R CMD check throws 1 warning for over 1,000 undocumented arguments in documentation objects.

Undocumented arguments in documentation object 'AGB.dbtable'
  'df' 'dbname' 'plot' 'code' 'censusno'
Undocumented arguments in documentation object 'AGB.ind'
  'df' 'dbhunit' 'plot' 'wsgdata' 'forest' 'ht.param' 'htmodel'
...(about 1,150 more)

Checking for missing documentation entries. All exported objects must be documented. See ?tools::undoc for more details.

http://r-pkgs.had.co.nz/check.html

This may take long to fix but important. Some arguments seems easy to define because they are defined somewhere else or because they are obvious. But many others are not not obvious and I may need help from those who have used the functions before.

One source of useful information may be the version of CTFS that is archived on CRAN, which has relatively good documentation. However, that version contains only 88 functions, versus about 400 that has the complete CTFS R Package.

• Fix errors in functions below; they likely err because they along the way of calling linear.model

  • growth.flexbin()
  • run.growthfit.bin()
  • run.growthbin.manyspp()

"Error in x %*% b : requires numeric/complex matrix/vector arguments". (Seems to be called from linear.model.ctr.) This limits running these other funtions:

I tried as.matrix(b) but did not solve the problem, neither other less obvious things that I tried. Because linear.model() seems to work, I suspect that the problem is in some function in between the ones listed above and linear.model(). Also, these functions fail likely because they involve linear.model():

- needs the output of `gwoth.flexibin()`
    - `graph.growthmodel.spp()`

- needs the output of `run.growthbin.manyspp()`
    - `compare.growthbinmodel()`
    - `overlay.growthbinmodel()`

• fix wavelet.allsp(); it errs with message:

"Error in dimnames(variance) <- list(names(splitdata), paste("scale", 1:ncol(variance))) : length of 'dimnames' [1] not equal to array extent"

• Avoid with() (non-standard evaluation) in:

  • wavelet.allsp(),
  • NeighborDensities(),
  • NDcount()

• fix modelBayes(); it calls debug before R throws error. At first, it erred calling subset(), so I replaced subset() by [, but continues to call debug from inside the function.

To enhance

• For all suggested packages, use requireNamespace("package", quietly = TRUE) to test if package is installed, then usepackage::fun()` to refer to functions.

• Address one note with multiple components

Before working on it, consider:

If you're not submitting to CRAN, carefully read each NOTE, but don’t go out of your way to fix things that you don’t think are problems

--http://r-pkgs.had.co.nz/check.html

checking R code for possible problems ... NOTE

Found an obsolete/platform-specific call in the following functions:
  'compare.growthbinmodel' 'define.graphwindow' 'graph.abundmodel'
  'graph.growthmodel' 'graph.modeldiag' 'graph.outliers' 'imageGraph'
  'image_dataframe' 'map2species' 'maptopo' 'overlay.growthbinmodel'
  'regsum'

Found the platform-specific devices:
  'X11' 'quartz' 'win.graph' 'win.metafile' 'x11'
dev.new() is the preferred way to open a new device, in the unlikely
event one is needed.

AGB.dbtable: no visible global function definition for 'odbcConnect'
AGB.dbtable: no visible global function definition for 'odbcClose'
...(about 30 more)

Undefined global functions or variables:
  as.points bci.full1 bci.full2 bci.full3 bci.full6 bci.split6
  bci.spptable bci3.spp composeParam.GaussianMap contour.quaddata
  coords2 ctfs.elev date.mdy dbh decimal.form gen.logistic index
  insideRectange inter khat korup.spp line.intersection mvrnorm
  odbcClose odbcConnect plotspp riwish samplemapfile sp spp20 sqlQuery
  trim.growth.mismeasure wsg.ctfs2

Found the following calls to attach():
File 'ctfs/R/utilities.R':
  attach(file)

• Document data accurately. Now, BCI data is available from the bci package, so old references to data should change to new references. For example:

  • the new name of bci.full1 is bci12full1,
  • of bci.stem4 is bci12stem4,
  • of bci.spptable is bci12spptable
  • and bci also contains bci_elevation and bci_habitatat

• Add example data. These functions lack a file or data to test the function or run examples

  • fullplot.imageJ()
  • rearrangeSurveyData()`
  • solve.topo()

• Avoid side effect in run.growthbin.manyspp(); it saves object to working directory without warning on the console or description in documentation.

• graph.abundmodel() transforms and prints data and plots data, should do 1 thing. Best to plot and return the first argument invisibly.

Side-effects functions should “invisibly” return the first argument, so that while they’re not printed they can still be used in a pipeline.

--http://r4ds.had.co.nz/functions.html

• Consider improving performance of model.littleR.Gibbs() and fitSeveralAbundModel(); a test took about 20 minutes to run. If important, I may search the bottlenecks.

• Enhance source code of functions listed below to defensively avoid non standard evaluation (not appropriate for programming) by replacing subset() with [. They were not fixed yet because, for the reasons listed below, I can't run the functions, and therefore I can't ensure that my fix doesn't brake some other part of the code.

  • function errs:
    • run.growthbin.manyspp()
  • function lacks required input:
    • imageJ.to.lxly()
  • data is missing to run the function
    • solve.topo()
    • rearrangeSurveyData()

To deprecate

These functions should be deprecated because they use attach() - graph.abundmodel(): Argument datafile passed to graph.abundmodel() may be deprecated because it uses attach() (see Good practice in ?attach). The function documentation now includes a warning.

To keep in mind, but disregard for now

• R CMD check throws one additional warning that I disregard for now:

'qpdf' is needed for checks on size reduction of PDFs

I think this error is related to a glitch in R CMD check

https://github.com/krlmlr/r-appveyor/issues/24

...a package that has an HTML vignette (but no PDF vignette) failed R CMD check --as-cran ... I think the warning originates ... due to a premature check for the existence of qpdf

https://stat.ethz.ch/pipermail/r-devel/2015-October/071917.html

ctfs 0.0.0.9004

• Document process to build package in data-raw/src2doc_make_step1.R and ...step2.R

• Solve most important warnings during package checks. Most notably, amend NAMESPACE after documenting with roxygen. Because function names use the format function.name, roxygen exports some functions as S3 methods. This is a problem that arises during package checks. Now, this is solved by amending NAMESPACE, but later versions should replace "." by "_" in function names.

• Combine roxygen documentation and source code into a single file

• Import package date, stated as a dependency on original CTFS documentation

• In a vignette, report complexity of ctfs and compare with other complex and simple packages.

• Create and document data object month names that is presumably crucial.

ctfs 0.0.0.9003

• Tidy up roxygen documentation. Now empty lines span 2 lines or less.

• Accept R markdown in roxygen documentation. This allows lists to display nicely.

• Solve warnings that result from empty descriptions.

ctfs 0.0.0.9002

• Solve warnings listed in ctfs 0.0.0.9001. Some warnings remain, but to solve those, we need to improve the documentation (not simply reformat available documentation), for example:

Warning: found non-ASCII strings

Undocumented arguments in documentation object 'AGB.dbtable'
  'df' 'dbname' 'plot' 'code' 'censusno'

checkRd: (5) bad.binsdpar.Rd:0-12: Must have a \description

ctfs 0.0.0.9001

• Document function names. (Source code was removed to bypass checks that fail and block the package from building). Help files are now available with ?<FUNCTION_NAME>, help(<FUNCTION_NAME>) or online at https://forestgeo.github.io/ctfs/.

• I still need to solve multiple warnings

Warning: @param [spatial_RipUvK.R#28]: mismatched braces or quotes
Warning: @author [spatial_block.analysis.R#13]: mismatched braces or quotes
Warning: @examples [spatial_block.analysis.R#69]: mismatched braces or quotes
Warning: @examples [spatial_quadfunc.R#353]: mismatched braces or quotes
Warning: @examples [topography_slope.R#65]: mismatched braces or quotes
Warning: @examples [topography_slope.R#169]: mismatched braces or quotes
Warning: @examples [topography_slope.R#218]: mismatched braces or quotes
Warning: @examples [topography_solvetopo.R#59]: mismatched braces or quotes
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forestgeo/ctfs documentation built on May 3, 2019, 6:44 p.m.