"field1","field2","field3","entity","class","taxonSpecific","constrainedValues","family","examples","description"
"authorship",NA,NA,"rmmName","character","no","NULL","base","MerowMaitnerOwensKassEnquistGuralnick_2018_Acer_Maxent_b3","Use the format Author_Year_Taxa_Model_fw, where the last two characters (here, fw) are alphanumeric and random."
"authorship",NA,NA,"names","character vector","no","NULL","base","Merow, Cory and Maitner, Brian and Owens, Hannah and Kass, Jamie and Enquist, Brian and Guralnick, Rob;","The names of those who created this model. Use the format Last, First and Last, First and Last, First, following bibtex conventions."
"authorship",NA,NA,"license","character","no","CC; CC BY; CC BY-SA; CC BY-ND; CC BY-NC; CC BY-NC-SA; CC BY-NC-ND; other","base","CC; CC BY; CC BY-SA; CC BY-ND; CC BY-NC; CC BY-NC-SA; CC BY-NC-ND","The license under which this model has been produced.  See https://creativecommons.org/licenses/ for common options."
"authorship",NA,NA,"contact","character","no","NULL","base","my.name@gmail.com","An email address for someone responsible for creating this model."
"authorship",NA,NA,"relatedReferences","character vector","no","NULL","base","@article{maitner2017bien,title={The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database},author={Maitner, Brian S and Boyle, Brad and Casler, Nathan and Condit, Rick and Donoghue, John and Dur{\'a}n, Sandra M and Guaderrama, Daniel and Hinchliff, Cody E and J{\o}rgensen, Peter M and Kraft, Nathan JB and others},journal={Methods in Ecology and Evolution}, year={2017},publisher={Wiley Online Library}}","A single bibtex format citation or vector of citations; User specified"
"authorship",NA,NA,"authorNotes","character","no","NULL","base","All authors contributed equally to the production of these models.","User specified. Any information related to author/ownership."
"authorship",NA,NA,"miscNotes","character","no","NULL","base","Funded by NSF grant xxxx","User specified; Any context for the models that didn't fit elsewhere"
"authorship",NA,NA,"doi","character","no","NULL","base","https://doi.org/10.1111/j.1600-0587.2013.07872.x","Digital Object Identifier for manuscripts related to the model"
"studyObjective",NA,NA,"purpose","character","no","explanation; prediction; projection","base","projection","See definitions in  Araujo et al. (2019, Science Advances;  10.1126/sciadv.aat4858)"
"studyObjective",NA,NA,"rangeType","character","no","potential;suitableArea;fundamental;realized;occupied; other;","base","potential; realized","Type of range the study focuses on. Use 'potential' if describing suitable habitat that may be occupied or unoccupied. Use 'realized' when describing only locations that are occupied."
"studyObjective",NA,NA,"invasion","character","no","native;invasive;naturalized;colonized; other;","base","native; invasive; naturalized; colonized","If an invasion study, what type of data is the model fitted with. If not an invasion study, set to NULL."
"studyObjective",NA,NA,"transfer","character","no","native;invasive;naturalized;colonized; other;","base","native; invasive; naturalized; colonized","If an invasion study, what type of data is the model transferred to. If not an invasion study, set to NULL."
"studyObjective",NA,NA,"assumptions","character","no","NULL","ODMAP","Species data are free from observational bias.","Specify the underlying model assumptions. Critical assumptions may relate to species-environment relationship, niche filling, data biases, and niche conservatism when attempting transfers."
"studyObjective",NA,NA,"hypotheses","character","no","NULL","ODMAP","We expect unimodal response to temperature.","State hypotheses about species-environment relationship."
"studyObjective",NA,NA,"workflow","character","no","NULL","ODMAP","Prior to model building, all predictor variables were standardised. We then tested for multicollinearity and retained only weakly correlated variables. Predictive model performance of GLMs was assessed using a 5-fold spatial block cross-validation.","Provide a conceptual description of modelling steps  including model fitting, assessment and prediction"
"data","occurrence",NA,"samplingDesign","character","no","NULL","ODMAP","A random stratified sampling design was used to identify sampling sites.","Provide details on the underlying sampling design, incl. spatial design (e.g. random, uniform, stratified), temporal design, nestedness"
"data","occurrence",NA,"taxon","character vector","yes","NULL","base","Acer rubrum; Nasua_nasua;","A character vector containing all of the units (typically taxon names) used in the model(s). Other phylogroups or operational taxonomic units are also acceptable."
"data","occurrence",NA,"dataType","character vector","no","presence only; presence absence; abundance; occupancy; other","base","presence only;  presence-absence; abundance","The type of records used in modeling. If multiple types are combined this can be a vector. For example some point process modeling frameworks combine presence only and presence absence data so this would be specified (in R) as c('presence only', 'presences absence')."
"data","occurrence",NA,"occurrenceType","character vector","no","breeding;wintering;migratory;resident; other;","NULL","breeding; wintering; migratory; resident","If applicable, see examples, else NULL."
"data","occurrence",NA,"yearMin","integer vector","optional","NULL","base","1900","If a temporal range of records was used, this is the earliest year used.  Absence or NA implies that there was no lower limit."
"data","occurrence",NA,"yearMax","integer vector","optional","NULL","base","2000","If a temporal range of records was used, this is the latest year used.  Absence or NA implies there is no upper boundary used."
"data","occurrence",NA,"sources","character vector","optional","NULL","base","@article{maitner2017bien,title={The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database},author={Maitner, Brian S and Boyle, Brad and Casler, Nathan and Condit, Rick and Donoghue, John and Dur{\'a}n, Sandra M and Guaderrama, Daniel and Hinchliff, Cody E and J{\o}rgensen, Peter M and Kraft, Nathan JB and others},journal={Methods in Ecology and Evolution}, year={2017},publisher={Wiley Online Library}}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified."
"data","occurrence",NA,"presenceSampleSize","integer vector","yes","NULL","occurrence, po, pa","87","Provide a vector of values in the order that taxa are listed in data$taxaVector"
"data","occurrence",NA,"absenceSampleSize","integer vector","yes","NULL","occurrence, pa","96","Provide a vector of values in the order that taxa are listed in data$taxaVector. Only relevant if data:occurrence:bagroundSampleSizeSet or ...Rule is not specified."
"data","occurrence",NA,"backgroundSampleSizeSet","integer vector","optional","NULL","occurrence, po","1000","Provide a vector of values in the order that taxa are listed in data$taxaVector or a single value applied all taxa"
"data","occurrence",NA,"backgroundSampleSizeRule","character","no","NULL","occurrence, po","All cells in convex hull around sample locations buffered by 500 km.","Provide a vector of values in the order that taxa are listed in data$taxaVector or a single value applied to all taxa"
"data","occurrence",NA,"spatialAccuracy","character","no","NULL","base","All observations are recorded to within 30m accuracy based on GPS","User specified. It is useful to specify information about the minimum and maximum acccuracy, or their distribution."
"data","environment",NA,"variableNames","character vector","optional","bio1; bio2; bio3; bio4; bio5; bio6; bio7; bio8; bio9; bio10; bio11; bio12; bio13; bio14; bio15; bio16; bio17; bio18; bio19; wc2.0_bio_10m_01; wc2.0_bio_5m_01; wc2.0_bio_1m_01; wc2.0_bio_10m_02; wc2.0_bio_5m_02; wc2.0_bio_1m_02; wc2.0_bio_10m_03; wc2.0_bio_5m_03; wc2.0_bio_1m_03; wc2.0_bio_10m_04; wc2.0_bio_5m_04; wc2.0_bio_1m_04; wc2.0_bio_10m_05; wc2.0_bio_5m_05; wc2.0_bio_1m_05; wc2.0_bio_10m_06; wc2.0_bio_5m_06; wc2.0_bio_1m_06; wc2.0_bio_10m_07; wc2.0_bio_5m_07; wc2.0_bio_1m_07; wc2.0_bio_10m_08; wc2.0_bio_5m_08; wc2.0_bio_1m_08; wc2.0_bio_10m_09; wc2.0_bio_5m_09; wc2.0_bio_1m_09; wc2.0_bio_10m_10; wc2.0_bio_5m_10; wc2.0_bio_1m_10; wc2.0_bio_10m_11; wc2.0_bio_5m_11; wc2.0_bio_1m_11; wc2.0_bio_10m_12; wc2.0_bio_5m_12; wc2.0_bio_1m_12; wc2.0_bio_10m_13; wc2.0_bio_5m_13; wc2.0_bio_1m_13; wc2.0_bio_10m_14; wc2.0_bio_5m_14; wc2.0_bio_1m_14; wc2.0_bio_10m_15; wc2.0_bio_5m_15; wc2.0_bio_1m_15; wc2.0_bio_10m_16; wc2.0_bio_5m_16; wc2.0_bio_1m_16; wc2.0_bio_10m_17; wc2.0_bio_5m_17; wc2.0_bio_1m_17; wc2.0_bio_10m_18; wc2.0_bio_5m_18; wc2.0_bio_1m_18; wc2.0_bio_10m_19; wc2.0_bio_5m_19; wc2.0_bio_1m_19; other","base","bio1, bio4, bio12, bio15","Environmental  variable names, preferably as they appear in the data source."
"data","environment",NA,"minVal","numeric vector","optional","NULL","transferEnv2","[{""bio1"":-273,""bio12"":0,""bio15"":0,""bio2"":9}]","Supply a vector of minimum values in JSON format for all predictors used in modeling (even if each predictor was not used for each species)"
"data","environment",NA,"maxVal","numeric vector","optional","NULL","transferEnv2","[{""bio1"":292,""bio12"":10017,""bio15"":246,""bio2"":212}]","Supply a vector of maximum values in JSON format for all predictors used in modeling (even if each predictor was not used for each species)"
"data","environment",NA,"yearMin","integer vector","optional","NULL","base","1970","The first year of the temporal range of climate data in the environmental layers."
"data","environment",NA,"yearMax","integer vector","optional","NULL","base","2000","The last year of the temporal range of climate data in the environmental layers."
"data","environment",NA,"extentSet","numeric vector","optional","NULL","base","[{""xmin"":-5582581.9734,""xmax"":5367418.0266,""ymin"":-7389365.067,""ymax"":7410634.933}]","The minimum longitude, maximum longitude, minimum latitude, maximum latitude of the bounding box defining the study area in JSON format. Specify in this order, separated by commas."
"data","environment",NA,"extentRule","character","optional","NULL","base","Buffered all points by 500km and joined the resulting polygons;","User specified"
"data","environment",NA,"resolution","numeric vector","no","NULL","base","1km","The spatial resolution and units of the environmental raster. Units should be a commonly used type such as m, km, or degrees. From Araujo et al. 2019, Sci. Adv.:The gold standard for the spatial and temporal resolution of predictor variables is to match the spatial resolution at which the response variable is affected by the predictors [see 1D]. Choosing an inappropriate spatial resolution can obscure the species– environment relationship and result in reduced predictive ability (113, 114). In addition, temporal resolution should match the biology of the response variable. For example, for seasonally migrating species, predictor variables should provide information throughout the year, rather than annual averages (115). Similarly, because the spatial distributions of many taxa show delayed responses to changes in predictor variables (116-120), analysis of historical time periods may provide better insights (94, 96, 97). Adjusting the spatial resolution of predictor variables to an appropriate (theoretically justified) resolution by downscaling or interpolation (65) is only acceptable when direct measurements are unavailable."
"data","environment",NA,"projection","character","optional","NULL","base","""+proj=laea +lat_0=15 +lon_0=-80 +x_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs=0,0,0""","The projection of the coordinate reference system (CRS)"
"data","environment",NA,"sources","character vector","no","NULL","base","@misc{hijmans2004worldclim,title={The WorldClim interpolated global terrestrial climate surfaces. Version 1.3},author={Hijmans, RJ and Cameron, SE and Parra, JL and Jones, PG and Jarvis, A},year={2004}}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified. To assign different sources explicitly to different layers, use JSON formatting such as [{""bio1"":""bibtex entry 1"",""bio12"":""""bibtex entry 2""}]"
"data","environment",NA,"notes","character vector","no","NULL","base","NULL","User specified; useful to explain whether environmentl layers have been reprojected after download or otherwise processed."
"data","observation",NA,"variableNames","character vector","optional","NULL","base","distanceToRoad, elevation, humanPopulationDensity","Variable names describing covariates of an observation model, preferably as they appear in the data source."
"data","observation",NA,"minVal","numeric vector","optional","NULL","base","[{""distanceToRoad"":0,""elevation"":0,""humanPopulationDensity"":0}]","Supply a vector of minimum values in JSON format for all predictors used in modeling of observation processes (even if each predictor was not used for each species)"
"data","observation",NA,"maxVal","numeric vector","optional","NULL","base","[{""distanceToRoad"":312,""elevation"":1234,""humanPopulationDensity"":26}]",NA
"data","transfer","environment1","minVal","numeric vector","optional","NULL","transferEnv1","[{""bio1"":-273,""bio12"":0,""bio15"":0,""bio2"":9}]","Supply a vector of minimum values in JSON format for all predictors used in modeling (even if each predictor was not used for each species). Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","maxVal","numeric vector","optional","NULL","transferEnv1","[{""bio1"":292,""bio12"":10017,""bio15"":246,""bio2"":212}]","Supply a vector of maximum values in JSON format for all predictors used in modeling (even if each predictor was not used for each species). Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","yearMin","integer","no","NULL","transferEnv1","2040","A single year indicating the beginning of the climate/weather record used for transfer. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","yearMax","integer","no","NULL","transferEnv1","2050","A single year indicating the end of the climate/weather record used for transfer. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","sources","character vector","no","NULL","transferEnv1","@misc{hijmans2004worldclim,title={The WorldClim interpolated global terrestrial climate surfaces. Version 1.3},author={Hijmans, RJ and Cameron, SE and Parra, JL and Jones, PG and Jarvis, A},year={2004}}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","extentSet","numeric vector","no","NULL","transferEnv1","[{""xmin"":-5582581.9734,""xmax"":5367418.0266,""ymin"":-7389365.067,""ymax"":7410634.933}]","The minimum longitude, maximum longitude, minimum latitude, maximum latitude of the bounding box defining the study area in JSON format. Specify in this order, separated by commas. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","extentRule","character","no","NULL","transferEnv1","Buffered all points by 500km and joined the resulting polygons","Text describing a generic rule for choosing the extent for multiple taxa. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","resolution","character","no","NULL","transferEnv1","1km","The spatial resolution and units of the environmental raster. Units should be a commonly used type such as m, km, or degrees. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data","transfer","environment1","notes","numeric","no","NULL","transferEnv1","Corresponds to RCP4.5","User specified. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"data",NA,NA,"dataNotes","character","optional","NULL","base","Worldclim v1.4","User specified"
"dataPrep","geographic","geographicStandardization","rule","character","no","GNRS;  not needed; other;","dataPrep","Geographic Name Resolution Service (GNRS); other;","If an algorithm was used to standardize geographic or political names, which algorithm was used?"
"dataPrep","geographic","geographicStandardization","notes","character","no","NULL","dataPrep","Countries that could not be matched to GADM were manually corrected","User specified"
"dataPrep","geographic","geographicalOutlierRemoval","rule","character","no","NULL","dataPrep","Countries with fewer than 5 records were excluded","If points were excluded on the basis of  being a geographic outlier, what criteria or algorithm was used?"
"dataPrep","geographic","geographicalOutlierRemoval","notes","character","no","NULL","dataPrep","Geographical outliers were only removed for species with more than 10 occurrences","User specified"
"dataPrep","geographic","centroidRemoval","rule","character","no","NULL","dataPrep","exact match; 0.98 likelihood score","If values were suspected centroids of geographic polygons and thus removed, what criteria were used to perform the removal?"
"dataPrep","geographic","centroidRemoval","notes","character","no","NULL","dataPrep","Centroid analysis included both WGS84 and LEA centroids","User specified"
"dataPrep","geographic","pointInPolygon","rule","character","no","country; state; county; not needed; other;","dataPrep","country; state; county","If performing checks to determine whether or not points overlap with polygons, what geopolitical levels were the checks performed for?"
"dataPrep","geographic","pointInPolygon","notes","character","no","NULL","dataPrep","Point-in-polygon testing was done at the lowest declared political division","User specified"
"dataPrep","geographic","altitudeRemoval","rule","character","no","NULL","dataPrep","Altitude within 250 m of reported value; not needed","If records were removed for having elevations reported that differ significantly from some source, what method was used?"
"dataPrep","geographic","spatialThin","rule","character","no","NULL","dataPrep","10 km; max distance between points; 100 km grid cells","If thinning in geographic space was performed, what distance was used to thin?"
"dataPrep","geographic","spatialThin","notes","character","no","NULL","dataPrep","Used function thin from R package spthin; used function gridSample from R package dismo","User specified"
"dataPrep","biological","duplicateRemoval","rule","character","no","environmental; point; cell; other; NA","dataPrep","environmental; coordinate; other (specify in Notes)","If duplicate records were removed, what criteria were used for removal?"
"dataPrep","biological","duplicateRemoval","notes","character","no","NULL","dataPrep","Duplicates removed for species with >10 occurrences; occurrences with duplicate county removed","User specified"
"dataPrep","biological","questionablePointRemoval","rule","character","no","NULL","dataPrep","latitude or longitude exactly 0; latitude or longitude an integer","If points were excluded on the basis of some criteria, what were they?"
"dataPrep","biological","questionablePointRemoval","notes","character","no","NULL","dataPrep","Points that corresponded to 100 km Lambert equal area centroids were removed","User specified"
"dataPrep","biological","taxonomicHarmonization","rule","character","no","TNRS; not needed; other;","dataPrep","Taxonomic Name Resolution Service (TNRS)","If taxonomic harmonization was performed, which algorithm was used?"
"dataPrep","biological","taxonomicHarmonization","notes","character","no","NULL","dataPrep","Taxa with no opinion in the TNRS were checked against ITIS.gov","User specified"
"dataPrep","biological","cultivatedRemoval","rule","character","no","CSR; not needed; other;","dataPrep","CSR (Cultivated Status Resolver); not needed","If cultivated records were removed, what algorithm or criteria were used?"
"dataPrep","biological","cultivatedRemoval","notes","character","no","NULL","dataPrep","Cultivated invasives were not removed;","User specified"
"dataPrep","biological","nonNativeRemoval","rule","character","no","NSR; not needed; other;","dataPrep","NSR (Native Status Resolver); not needed;","If introduced species were removed, what method/ source was used to assess native status?"
"dataPrep","biological","nonNativeRemoval","notes","character","no","NULL","dataPrep","NULL","User specified"
"dataPrep","environmental","environmentalOutlierRemoval","rule","character","no","NULL","dataPrep","algorithm name; not needed;","If environmental outliers were removed, which algorithm was used to determine outlier status?"
"dataPrep","environmental","environmentalOutlierRemoval","notes","character","no","NULL","dataPrep","NULL","User specified"
"dataPrep","environmental","environmentalThin","rule","character","no","NULL","dataPrep","10 units in environmental space","If thinning in environmental space was performed, how many units were used to thin?"
"dataPrep","environmental","environmentalThin","notes","character","no","NULL","dataPrep","Used function gridSample from R package dismo on PCA of environmental variables","User specified"
"dataPrep","environmental",NA,"notes","character","no","NULL","dataPrep","NULL","User specified"
"dataPrep",NA,NA,"dataPrepNotes","character vector","optional","NULL","base","NULL","User specified"
"model",NA,NA,"algorithms","character vector","optional","generalized linear model; generalized additive model; boosted regression trees; maxent; bioclim;  Poisson point process; range bagging; all biomod2 models; maxnet; other;","base","generalized linear model; generalized additive model; boosted regression trees; maxent; bioclim;  Poisson point process; range bagging; all biomod2 models;","Specify the algorithm by name; see examples."
"model",NA,NA,"algorithmCitation","character vector","no","NULL","base","@article{title={Maximum entropy modeling of species geographic distributions},author={Phillips, Steven J and Anderson, Robert P and Schapire, Robert E},journal={Ecological modelling},volume={190},number={3},pages={231--259},year={2006},publisher={Elsevier}}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified."
"model",NA,NA,"speciesCount","integer","no","NULL","base","1","Total number of species (or other operational taxonomic units) in the study. All taxon-specific and optionally taxon-specific entities must be vectors of this length, or 1, as appropriate."
"model",NA,NA,"covariateScaling","character","no","untransformed;  z-score; unit interval; other;","NULL","untransformed;  z-score; unit interval; log;","Describe any common approach to rescaling covariates by name."
"model",NA,NA,"occurrenceTreatedAs","character","no","point; cell; transect; polygon; other;","NULL","point; cell; transect; polygon;","Describe the spatial unit corresponding to a sample."
"model",NA,NA,"selectionRules","character","no","NULL","base","AIC; highest test AUC; TSS; etc.","Describe the model selection method(s) used."
"model",NA,NA,"finalModelSettings","character","no","NULL","base","LQH3 (Maxent); var1 + var2 + var1*var2 (regression); etc.","Describe the model chosen via stated selection methods."
"model",NA,NA,"notes","character","no","NULL","base","NULL","User specified"
"model","partition",NA,"occurrenceSubsampling","character","no","5-fold cross validation; none; other;","NULL","K-fold cross validation; none; bootstrap; random","Describe common methods by name or uncommon methods by bibtex style reference."
"model","partition",NA,"numberFolds","numeric","no","NULL","NULL","4","Number of folds used in k fold cross validation"
"model","partition",NA,"partitionSet","character","no","NULL","base","spatial blocks defined a priori","Partitioning method used to split the data. Note that this differs from modelFit:resampling in that partitioning refers to independent subsets (e.g., training vs evaluation data or k-fold cross validation) whereas resampling involves sampling with replacement, and might be done as part of a modeling step on a partition of the data."
"model","partition",NA,"partitionRule","character","no","NULL","base","spatial blocks defined by k means clustering","User specified. Note that this differs from modelFit:resampling in that partitioning refers to independent subsets (e.g., training vs evaluation data or k-fold cross validation) whereas resampling involves sampling with replacement, and might be done as part of a modeling step on a partition of the data."
"model","partition",NA,"notes","character","no","NULL","base","background points also partitioned","User specified"
"model","resampling",NA,"resamplingRule","character","no","NULL","NULL","repeated split-sample, bootstrap repetitions","User specified; This is designed to accommodate multiple resampling repetitions of models with the same algorithm for a species. E.g.,  repeated split-sample, bootstrap repetitions, repeated models with different  background, etc."
"model","resampling",NA,"notes","character","no","NULL","NULL","NULL","User specified; This is designed to accommodate multiple resampling repetitions of models with the same algorithm for a species. E.g.,  repeated split-sample, bootstrap repetitions, repeated models with different  background, etc."
"model","samplingBias",NA,"notes","character","no","NULL","NULL","NULL","User specified. From Araujo et al. 2019 Sci. Adv.: Biological response data may be biased or not sufficiently complete (See 1A). The gold standard requires either evidence for lack of bias, or that bias is addressed fully by methods including an observation model (e.g., an occupancy model) (135), or by including covariates that explicitly control for the bias (136). As a gold standard, the efficacy of the bias correction can be assessed by comparison with independent data, such as from high quality surveys in different regions or times. The silver standard approach to bias correction weights the data by their reliability, and assesses the result through internal cross-validation. Minimally, likely biases are acknowledged and described, along with a discussion of the effects that they may have on interpretations."
"model","algorithm","maxent","featureSet","character vector","optional","L; LQ; LQP; LQPT; LQPTH;  H; HT","maxent","L; LQ; LQP; LQPT; LQPTH;  H; HT","Feature classes used, such as linear (L), quadratic (Q), etc. See maxent manual."
"model","algorithm","maxent","featureRule","character","no","NULL","maxent","L for <50 presences; LQ for >= 50 presences","Rules for choosing feature classes if different than Maxent's defaults. See maxent manual."
"model","algorithm","maxent","regularizationMultiplierSet","numeric vector","optional","NULL","maxent","1, 1.5, 2, 2.5, 3","Multiplier used for regularization via the LASSO penalty. Maxent's default is 1. See maxent manual"
"model","algorithm","maxent","regularizationRule","character","no","NULL","maxent","Chosen based on 5-fold cross validation on a grid of regularization multipliers from [0;10]","User specified rule to choose the regularization multiplier if different than Maxent's default. See maxent manual."
"model","algorithm","maxent","convergenceThresholdSet","numeric vector","optional","NULL","maxent","1.00E-05","Maxent's convergence threshold for terminating the fitting algorithm. See maxent manual"
"model","algorithm","maxent","samplingBiasRule","character","no","target group; offset; none; other;","maxent","target group; offset; none;","User specified rule to account for sampling bias. The most common method is target group, although sampling bias models can be used. See maxent manual and @article{author = {Phillips, SJ and Dudik, M and Elith, J and Graham, CH and Lehmann, A and Leathwick, J and Ferrier, S},title = {{Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data}},journal = {Ecological Applications},year = {2009},volume = {19},number = {1},pages = {181--197}}"
"model","algorithm","maxent","samplingBiasNotes","character","optional","NULL","maxent","NULL","Specify any relevant information useful for describing how sampling bias was modeled. This might include related taxa, in the case of a target group, or covariates (such as distance to road) used to model the bias."
"model","algorithm","maxent","targetGroupSampleSize","numeric vector","optional","NULL","maxent","5076","For use with Maxent or point process models to account for sampling bias. See: Phillips, S., M. Dudik, J. Elith, C. Graham, A. Lehmann, J. Leathwick, and S. Ferrier. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19:181–197."
"model","algorithm","maxent","offsetSet","character","no","NULL","maxent","sampling bias model","Describe the values of an offset used."
"model","algorithm","maxent","offsetRule","character","no","NULL","maxent","sampling bias model fit to target group of all proteas","Describe the method/rule used to generate an offset."
"model","algorithm","maxent","expertMapProbSet","numeric vector","optional","NULL","maxentExpertMap","0.9","Proportion of prior probability set inside the expert map boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","expertMapProbRule","character","no","NULL","maxentExpertMap","Based on proportion of presences correctly classified","Rule for setting proportion of prior probability set inside the expert map boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","expertMapRateSet","numeric vector","optional","NULL","maxentExpertMap","0.05","Decay rate of prior probability outside expert map boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","expertMapRateRule","character","no","NULL","maxentExpertMap","NULL","Rule for setting decay rate of prior probability outside expert map boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","expertMapSkewSet","numeric vector","optional","NULL","maxentExpertMap","0","Skew defines asymmetry in the decay rate outside the expert boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","expertMapSkewRule","character","no","NULL","maxentExpertMap","NULL","Rule for setting skew, which defines asymmetry in the decay rate outside the expert boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","expertMapShiftSet","numeric vector","optional","NULL","maxentExpertMap","0","Shift of decay relative to the expert map boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","expertMapShiftRule","character","no","NULL","maxentExpertMap",NA,"Rule for setting shift of decay relative to the expert map boundary. See @article{author = {Merow, Cory  and Wilson, Adam M and Jetz, Walter},title = {{Integrating occurrence data and expert maps for improved species range predictions}},journal = {Global Ecology and Biogeography},year = {2017},volume = {26},number = {2},pages = {243--258}}."
"model","algorithm","maxent","notes","character vector","optional","NULL","maxent","NULL","User specified"
"model","algorithm","ppm","formula","character","optional","NULL","ppm","~bio1 + I(bio1)^2 +bio4 + (bio4)^2+bio1*bio4","Specify the formula as you would in the software used"
"model","algorithm","ppm","fitting","character","optional","NULL","ppm","downweighted Poisson regression; infinitely weighted logistic regression","User specified text to indicate the method used for fitting. See, especially appendix of, @article{author = {Renner, Ian W and Elith, Jane and Baddeley, Adrian and Fithian, William and Hastie, Trevor and Phillips, Steven J and Popovic, Gordana and Warton, David I},title = {{Point process models for presence-only analysis}},journal = {Methods in Ecology and Evolution},year = {2015},volume = {6},number = {4},pages = {366--379}}"
"model","algorithm","ppm","notes","character vector","optional","NULL","ppm","NULL","User specified"
"model","ensemble",NA,"algorithms","character vector","optional","maxent; ppm; glm; mars; brt; bioclim; ann; gam; randomForest; rangeBagging;","ensemble, ODMAP","maxent; glm; mars; brt;","A vector of the algorithm names used in an ensemble model. Use constrained values if applicable for algorithm names."
"model","ensemble",NA,"weighting","character vector","optional","NULL","ensemble","Models weighted by AUC scores on test data;","Describe rules used for weighting models in the ensemble"
"model","ensemble",NA,"notes","character vector","optional","NULL","ensemble","NULL","User specified"
"model","algorithm","glm","family","character","no","binomial; poisson; negative binomial","glm","binomial; poisson; negative binomial","Distribution family"
"model","algorithm","glm","formula","character","optional","NULL","glm","~bio1 + I(bio1)^2 +bio4 + (bio4)^2+bio1*bio4","Specify the formula as you would in the software used"
"model","algorithm","glm","weights","character vector","character vector","NULL","glm","NULL","Weights on observations"
"model","algorithm","glm","notes","character vector","character vector","NULL","glm","NULL","User specified. Note the GLM stands for generalized linear model."
"model","algorithm","mars","formula","character vector","optional","NULL","mars","~bio1 + bio4 + bio7","Specify the formula as you would in the software used."
"model","algorithm","mars","degree","numeric vector","optional","NULL","mars","2","Maximum degree of interaction"
"model","algorithm","mars","penalty","numeric vector","optional","NULL","mars","3","Generlized Cross Validation penalty per knot"
"model","algorithm","mars","nk","numeric vector","optional","NULL","mars","min(200, max(20, 2 * ncol(x))) + 1","Maximum number of model terms before pruning. The default is semi-automatically calculated from the number of predictors but may need adjusting"
"model","algorithm","mars","thresh","numeric vector","optional","NULL","mars","0.001","Forward stepping threshold. Default is 0.001. This is one of the arguments used to decide when forward stepping should terminate"
"model","algorithm","mars","pmethod","character vector","optional","NULL","mars","""backward"" ""none"" ""exhastive""","Pruning method"
"model","algorithm","mars","notes","character vector","optional","NULL","mars","NULL","User specified. Note that MARS stands for multivariate adaptive regression splines."
"model","algorithm","brt","formula","character vector","optional","NULL","brt","~bio1 + bio4 + bio7","Specify the formula as you would in the software used."
"model","algorithm","brt","distribution","character vector","optional","NULL","brt","bernoulli; adaboost; gaussian; laplace; coxph","Distribution family"
"model","algorithm","brt","nTrees","numeric vector","optional","NULL","brt","100","See R function gbm::gbm; total number of trees"
"model","algorithm","brt","interactionDepth","numeric vector","optional","NULL","brt","1","See R function gbm::gbm; complexity of fitted tree"
"model","algorithm","brt","shrinkage","numeric vector","optional","NULL","brt","0.001","See R function gbm::gbm; learning rate"
"model","algorithm","brt","bagFraction","numeric vector","optional","NULL","brt","0.5","See R function gbm::gbm; fraction of data used to fit successive trees"
"model","algorithm","brt","trainFraction","numeric vector","optional","NULL","brt","1","See R function gbm::gbm; fraction of data used in training"
"model","algorithm","brt","notes","character vector","optional","NULL","brt","NULL","User specified. Note that BRT stands for boosted regression trees"
"model","algorithm","bioclim","notes","character vector","optional","NULL","bioclim","NULL","User specified"
"model","algorithm","ann","formula","character vector","optional","NULL","ann","nnet(Data, SpeciesPresenceAbsence,...)","Give the explanatory variable, then the vector of presence and absence, then the option values for size and decay."
"model","algorithm","ann","size","numeric","optional","NULL","ann","size=2 (hidden layers)","Number of hiddne layers"
"model","algorithm","ann","decay","numeric","optional","NULL","ann","decay=0.001","Learning rate"
"model","algorithm","ann","notes","character vector","optional","NULL","ann","NULL","User specified. Note that ANN stands for artificial neural network."
"model","algorithm","gam","family","character","no","binomial; poisson; negative binomial; tweedie; gaussian; gamma; zero-inflated poisson","gam",NA,"Distribution family"
"model","algorithm","gam","formula","character vector","optional","NULL","gam","~s(bio1, k=10) + s(bio4, k=10)","Specify the formula either as you would in the software used or in plain text. Formula indicates the smoothing function (degree of smoothing) used in gam(); s for splines, lo for loess; k is the dimension of the basis used to represent the smoothing term. Other packages such as mgcv() select smoothing parameters through cross-validation, not used specified"
"model","algorithm","gam","smoothTerms","character vector","optional","thin-plate spline (tp); thin-plate spline with shrinkage (ts); Duchon spline (ds); cubic regression spline (cr); cubic regression spline with shrinkage (cs); cyclic cubic regression spline (cc); spline on the sphere (sos); P-spline (ps); cyclic P-spline (cs); random effect spline (re); markov random fields (mrf); gaussian process (gp); soap film(so)","gam","~s(bio1, k=10, bs = ""tp"") + s(month, bs = ""cc"")","Type of smoothing spline used (mgcv package)."
"model","algorithm","gam","weights","character vector","optional","NULL","gam","NULL","Prior weights on the contribution of the data to the log likelihood. Note that a weight of 2, for example, is equivalent to having made exactly the same observation twice."
"model","algorithm","gam","offset","character vector","optional","NULL","gam","NULL","Can be used to supply a model offset for use in fitting. Note that this offset will always be completely ignored when predicting, unlike an offset included in formula."
"model","algorithm","gam","method","character vector","no","generalised cross validation (GCV); restricted maximum likelihood (REML); maximum likelihood (ML)","gam","NULL","Smoothing parameter estimation method."
"model","algorithm","gam","select","character vector","no","TRUE; FALSE","gam","NULL","Shrinkage penalty. If this is TRUE then gam can add an extra penalty to each term so that it can be penalized to zero. This means that the smoothing parameter estimation that is part of fitting can completely remove terms from the model."
"model","algorithm","gam","notes","character vector","optional","NULL","gam","NULL","User specified. Note that GAM stands for generalized additive model."
"model","algorithm","fda","formula","character vector","optional","NULL","fda","~ 1 + bio3 + bio7 + bio11 + bio12","Any common fomula"
"model","algorithm","fda","method","character vector","optional","NULL","fda","method=mars","Non-parametric functions such as MARS and BRUTO"
"model","algorithm","fda","notes","character vector","optional","NULL","fda","NULL","User specified. Note that FDA stands for flexible discriminant analysis."
"model","algorithm","randomForest","ntree","numeric vector","optional","NULL","randomForest","500","number of trees"
"model","algorithm","randomForest","mtry","numeric vector","optional","NULL","randomForest","3","number of variables randomly sampled as candidates in each split"
"model","algorithm","randomForest","maxnodes","numeric vector","optional","NULL","randomForest","4","maximum number of nodes per tree"
"model","algorithm","randomForest","try","logical","optional","NULL","randomForest","TRUE","should sampling of cases be done with or without replacement"
"model","algorithm","randomForest","notes","character vector","optional","NULL","randomForest","NULL","User specified"
"model","algorithm","rangeBagging","votes","numeric vector","optional","NULL","rangeBagging","100","Number of replicates"
"model","algorithm","rangeBagging","nDimensions","numeric vector","optional","NULL","rangeBagging","2","Number of environmental dimensions sampled in each replicate"
"model","algorithm","rangeBagging","proportionSubset","numeric vector","optional","NULL","rangeBagging","0.5","Proportion of presences sampled during each replicate"
"model","algorithm","rangeBagging","notes","character vector","optional","NULL","rangeBagging","NULL","User specified"
"model","algorithm","occupancy","formula","character vector","optional","NULL","occupancy","~bio1 + I(bio1)^2 +bio4 + (bio4)^2+bio1*bio4","Specify the formula used for occurrence either as you would in the software used or in plain text"
"model","algorithm","occupancy","observationFormula","character vector","optional","NULL","occupancy","~humanPopulationDensity","Specify the formula used for observation either as you would in the software used or in plain text"
"model","algorithm","occupancy","notes","character vector","optional","NULL","occupancy","NULL","User specified"
"model",NA,NA,"references","character vector","no","NULL","base","@article{Merow:2013eq,author = {Merow, Cory  and Smith, Matthew J and Silander, John A},title = {{A practical guide to MaxEnt for modeling species{\textquoteright} distributions: what it does, and why inputs and settings matter}},journal = {Ecography},year = {2013},volume = {36},number = {10},pages = {1058--1069}}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified."
"model","justification",NA,"modelComplexity","character vector",NA,"NULL","ODMAP","The model settings were chosen to yield simple, smooth response surfaces because we attempt extrapolation and the species may not be at equilibrium with the environment.","Provide a general description of model complexity"
"prediction","continuous",NA,"units","character","no","absolute probability; relative occurrence rate; other;","prediction","absolute probability;  relative occurrence rate; number of individuals; biomass (kg); biomass (g)","A description of the meaning and units of the prediction."
"prediction","continuous",NA,"minVal","numeric vector","yes","NULL","prediction","0.001","Minimum predicted value in study region."
"prediction","continuous",NA,"maxVal","numeric vector","yes","NULL","prediction","0.97","Maximum predicted value in study region."
"prediction","binary",NA,"thresholdSet","numeric vector","yes","NULL","base","0.34","Threshold value of predicted occurrence probability used to distinguish presence from absence for binary classification (e.g., truePositiveRate, falseNegativeRate)"
"prediction","binary",NA,"thresholdRule","numeric vector","optional","1% quantile of training presences; 5% quantile of training presences; 10% quantile of training presences; max (sensitivity + specificity); other","base","5% quantile of training presences","Rule for obtaining a threshold value of predicted occurrence probability used to distinguish presence from absence for binary classification (e.g., truePositiveRate, falseNegativeRate)"
"prediction",NA,NA,"notes","character vector","optional","NULL","prediction","NULL","User specified"
"prediction",NA,NA,"extrapolation","character","no","clamping; extrapolate function; other;","base","clamping; extrapolate function","In Example 2, 'extrapolate function' indicates that the fitted functional form was not modified beyond the range of the data."
"prediction","transfer","environment1","units","character","no","absolute probability;  relative occurrence rate; other;","transferEnv1","absolute probability;  relative occurrence rate; presence/absence","The units of the prediction, e.g., absolute probability (occupancy), relative probobility (relative occurrence rate), abdunance, relative abundance, etc. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"prediction","transfer","environment1","minVal","numeric vector","optional","NULL","transferEnv1","0.001","Supply a vector of minimum values all taxa predictions. For example, the first element is the minimum predicted value for taxon 1, the second element is the minimum predicted value for taxon 2, etc. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"prediction","transfer","environment1","maxVal","numeric vector","optional","NULL","transferEnv1","0.97","Supply a vector of maximum values for all taxa predictions. For example, the first element is the maximum predicted value for taxon 1, the second element is the maximum predicted value for taxon 2, etc. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"prediction","transfer","environment1","thresholdSet","numeric vector","optional","NULL","transferEnv1","0.34","only if binary/trinary. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"prediction","transfer","environment1","thresholdRule","numeric vector","no","NULL","transferEnv1","5% quantile of training presences","only if binary/trinary. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"prediction","transfer","environment1","extrapolation","character","no","clamping; extrapolate function; other;","base","clamping; extrapolate function","In Example 2, 'extrapolate function' indicates that the fitted functional form was not modified beyond the range of the data."
"prediction","transfer","environment1","notes","character vector","optional","NULL","transferEnv1","NULL","User specified. Additional environments can be added by including the field data:transfer:environment2: for each relevant entity."
"prediction","transfer",NA,"notes","character vector","optional","NULL","base","NULL","User specified"
"prediction","transfer",NA,"extrapolation","character","no","clamping; extrapolate function; other;","NULL","clamping; extrapolate function","In Example 2, 'extrapolate function' indicates that the fitted functional form was not modified beyond the range of the data."
"prediction","uncertainty",NA,"units","character","no","NULL","base","absolute probability; relative probability","Units of values on uncertainty map"
"prediction","uncertainty",NA,"minVal","numeric vector","yes","NULL","base","0.001","Minimum predicted value in study region."
"prediction","uncertainty",NA,"maxVal","numeric vector","yes","NULL","base","0.14","Maximum predicted value in study region."
"prediction","uncertainty",NA,"algorithmic","character","no","NULL","ODMAP","We quantify the error range in predictions originating from different algorithms using the standard deviation.","Specify how you accounted for algorithmic uncertainty in your model predictions."
"prediction","uncertainty",NA,"parameter","character","no","NULL","ODMAP","We quantify the error range in predictions originating from different candidate models using the standard deviation.","Specify how you accounted for parameter uncertainty in your model predictions."
"prediction","uncertainty",NA,"scenario","character","no","NULL","ODMAP","We quantify the error range in predictions originating from different climate scenarios using the standard deviation.","Specify how you accounted for uncertainty in scenarios in your model predictions."
"prediction","uncertainty",NA,"inputData","character","no","NULL","ODMAP","We compare predictions from models calibrated on different input data.","Specify how you accounted for uncertainty in input data in your model predictions."
"prediction","uncertainty",NA,"extrapolation","character","no","NULL","ODMAP","We used MESS (multivariate environmental similarity surface) maps to quantify environmental novelty in the transfer data.","Specify how you indentified/treated novel environments in your model transfers."
"prediction","uncertainty",NA,"notes","character vector","optional","NULL","base","NULL","User specified"
"assessment","trainingDataStats",NA,"AUC","numeric vector","yes","NULL","binaryClassification","0.923","Area Under the Curve (ROC curve); See R function ROCR::performance."
"assessment","trainingDataStats",NA,"pearsonCor","numeric vector","yes","NULL","binaryClassification","0.914","Pearson's correlation coefficient; See R function ROCR::performance."
"assessment","trainingDataStats",NA,"cohensKappa","numeric vector","yes","NULL","binaryClassification","0.842","Cohen's kappa statistic; See R function ROCR::performance."
"assessment","trainingDataStats",NA,"trueSkillStatistic","numeric vector","yes","NULL","binaryClassification","0.825","See R function ROCR::performance"
"assessment","trainingDataStats",NA,"truePositiveRate","numeric vector","yes","NULL","binaryClassification","0.95","See R function ROCR::performance"
"assessment","trainingDataStats",NA,"trueNegativeRate","numeric vector","yes","NULL","binaryClassification","0.7","See R function ROCR::performance"
"assessment","trainingDataStats",NA,"falsePositiveRate","numeric vector","yes","NULL","binaryClassification","0.3","See R function ROCR::performance"
"assessment","trainingDataStats",NA,"falseNegativeRate","numeric vector","yes","NULL","binaryClassification","0.05","See R function ROCR::performance"
"assessment","trainingDataStats",NA,"boyce","numeric vector","yes","NULL","binaryClassification","0.874","Boyce index; See R package ecospat"
"assessment","trainingDataStats",NA,"pAUC","numeric vector","yes","NULL","binaryClassification","0.843","See R package pAUC"
"assessment","trainingDataStats",NA,"pAUCLoThreshold","numeric vector","yes","NULL","binaryClassification","0.211","Lower threshold value used for calculating partial AUC"
"assessment","trainingDataStats",NA,"pAUCHiThreshold","numeric vector","yes","NULL","binaryClassification","0.564","Upper threshold value used for calculating partial AUC"
"assessment","trainingDataStats",NA,"AIC","numeric vector","yes","NULL","NULL","34.2","Akaike Information Criterion"
"assessment","trainingDataStats",NA,"BIC","numeric vector","yes","NULL","NULL","27.4","Bayesian Information Criterion"
"assessment","trainingDataStats",NA,"DIC","numeric vector","yes","NULL","NULL","19.6","Deviance Information Criterion"
"assessment","testingDataStats",NA,"AUC","numeric vector","yes","NULL","binaryClassification","0.923","Area Under the Curve (ROC curve); See R function ROCR::performance."
"assessment","testingDataStats",NA,"AUCDiff","numeric vector","yes","NULL","binaryClassification","0.001","Absolute difference between AUCtrain and AUCtest"
"assessment","testingDataStats",NA,"pearsonCor","numeric vector","yes","NULL","binaryClassification","0.914","Pearson's correlation coefficient; See R function ROCR::performance."
"assessment","testingDataStats",NA,"cohensKappa","numeric vector","yes","NULL","binaryClassification","0.842","Cohen's kappa statistic; See R function ROCR::performance."
"assessment","testingDataStats",NA,"trueSkillStatistic","numeric vector","yes","NULL","binaryClassification","0.825","See R function ROCR::performance"
"assessment","testingDataStats",NA,"truePositiveRate","numeric vector","yes","NULL","binaryClassification","0.95","See R function ROCR::performance"
"assessment","testingDataStats",NA,"trueNegativeRate","numeric vector","yes","NULL","binaryClassification","0.7","See R function ROCR::performance"
"assessment","testingDataStats",NA,"falsePositiveRate","numeric vector","yes","NULL","binaryClassification","0.3","See R function ROCR::performance"
"assessment","testingDataStats",NA,"falseNegativeRate","numeric vector","yes","NULL","binaryClassification","0.05","See R function ROCR::performance"
"assessment","testingDataStats",NA,"boyce","numeric vector","yes","NULL","binaryClassification","0.874","Boyce index; See R package ecospat"
"assessment","testingDataStats",NA,"omissionRate","numeric vector","yes","NULL","binaryClassification","0.125","Omission rate of occurrence points on test data"
"assessment","testingDataStats",NA,"notes","character vector","yes","NULL","binaryClassification","threshold set to minimum training presence","Used to state thresholds for threshold-dependent evaluation metrics, etc."
"assessment","evaluationDataStats",NA,"AUC","numeric vector","yes","NULL","binaryClassification","0.923","Area Under the Curve (ROC curve); See R function ROCR::performance."
"assessment","evaluationDataStats",NA,"pearsonCor","numeric vector","yes","NULL","binaryClassification","0.914","Pearson's correlation coefficient; See R function ROCR::performance."
"assessment","evaluationDataStats",NA,"cohensKappa","numeric vector","yes","NULL","binaryClassification","0.842","Cohen's kappa statistic; See R function ROCR::performance."
"assessment","evaluationDataStats",NA,"trueSkillStatistic","numeric vector","yes","NULL","binaryClassification","0.825","See R function ROCR::performance"
"assessment","evaluationDataStats",NA,"truePositiveRate","numeric vector","yes","NULL","binaryClassification","0.95","See R function ROCR::performance"
"assessment","evaluationDataStats",NA,"trueNegativeRate","numeric vector","yes","NULL","binaryClassification","0.7","See R function ROCR::performance"
"assessment","evaluationDataStats",NA,"falsePositiveRate","numeric vector","yes","NULL","binaryClassification","0.3","See R function ROCR::performance"
"assessment","evaluationDataStats",NA,"falseNegativeRate","numeric vector","yes","NULL","binaryClassification","0.05","See R function ROCR::performance"
"assessment","evaluationDataStats",NA,"boyce","numeric vector","yes","NULL","binaryClassification","0.874","Boyce index; See R package ecospat"
"assessment","trainingDataStats",NA,"metrics","character vector","no","AUC; TSS; Boyce; sensitivity; specificity;","ODMAP","AIC, BIC, DIC, AUC, true positive rate, true negative rate, false positive rate, false negative rate, Kappy, TSS","Indicate the performance statistics estimated on training data. Note that this entity is designed to include simply a vector of the metrics used for consistency with the ODMAP protocol. You might also consider including explict numeric values of different performance metrics under other entities in the assessment field, following the RMMS protocol."
"assessment","testingDataStats",NA,"metrics","character vector","no","AUC; TSS; Boyce; sensitivity; specificity;","ODMAP","AIC, BIC, DIC, AUC, true positive rate, true negative rate, false positive rate, false negative rate, Kappy, TSS","Indicate the performance statistics estimated on validation data. Note that this entity is designed to include simply a vector of the metrics used for consistency with the ODMAP protocol. You might also consider including explict numeric values of different performance metrics under other entities in the assessment field, following the RMMS protocol"
"assessment","evaluationDataStats",NA,"metrics","character vector","no","AUC; TSS; Boyce; sensitivity; specificity;","ODMAP","AIC, BIC, DIC, AUC, true positive rate, true negative rate, false positive rate, false negative rate, Kappy, TSS","Indicate the performance statistics estimated on (independent) test data. Note that this entity is designed to include simply a vector of the metrics used for consistency with the ODMAP protocol. You might also consider including explict numeric values of different performance metrics under other entities in the assessment field, following the RMMS protocol"
"assessment",NA,NA,"expertJudgement","character","no","NULL","ODMAP","The predicted range was compared against expert judgement.","Describe any plausibility checks based on expert judgements, e.g. map displays."
"assessment",NA,NA,"responseCurves","character vector","no","partial response plot, evaluation strip, inflated response","ODMAP","We used inflated response curves to visualize model behavior.","Methods used to visualize response plots"
"assessment",NA,NA,"references","character vector","no","NULL","base","@article{Liu:2010cq, author = {Liu, Canran and White, Matt and Newell, Graeme},title = {{Measuring and comparing the accuracy of species distribution models with presence-absence data}},year = {2010},volume = {34},number = {2},pages = {232--243}}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified."
"assessment",NA,NA,"notes","character vector","optional","NULL","base","NULL","User specified"
"code","wallace",NA,"occsNum","numeric","yes","NULL","wallace","NULL","Internal use only in Wallace. Note that this may differ from data:occurrence:pesenceSampleSize because users may subset the presence points in Wallace."
"code","wallace",NA,"userCSV","character","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","wallace",NA,"removedIDs","numeric vector","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","wallace",NA,"occsCellPolyCoords","numeric vector","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","wallace",NA,"userBgExt","character","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","wallace",NA,"userBgPath","character","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","wallace",NA,"userBgShpParams","character","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","wallace",NA,"maxentEvalPlotCell","character","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","wallace",NA,"bcPlotSettings","character","yes","NULL","wallace","NULL","Internal use only in Wallace."
"code","software",NA,"platform","character vector","no","NULL","base","@Manual{title = {R: A Language and Environment for Statistical Computing},author = {{R Core Team}},organization = {R Foundation for Statistical Computing},address = {Vienna, Austria},year = {2017},url = {https://www.R-project.org/},}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified."
"code","software",NA,"packages","character vector","no","NULL","base","@Manual{title = {dismo: Species Distribution Modeling},author = {Robert J. Hijmans and Steven Phillips and John Leathwick and Jane Elith},year = {2017}, note = {R package version 1.1-4},url = {https://CRAN.R-project.org/package=dismo},}","bibtex format citation or vector of citations or a traditional reference format as specified by a journal or user specified."
"code",NA,NA,"demoCodeLink","character","optional","NULL","base","https://github.com/cmerow/Merow_et_al_2016_GEB_Minxent_Examples","A permanent link to a website that contains simplified examples of the analysis."
"code",NA,NA,"vignetteCodeLink","character","optional","NULL","base","https://cran.r-project.org/web/packages/bossMaps/vignettes/bossMaps.html","A permanent link to an R-style vignette."
"code",NA,NA,"fullCodeLink","character","optional","NULL","base","https://github.com/cmerow/Merow_et_al_2016_GEB_Minxent_Examples","A permanent link to a website that contains complete code for the analysis."
"code",NA,NA,"demoDataLink","character","optional","NULL","base","https://github.com/cmerow/Merow_et_al_2016_GEB_Minxent_Examples","A permanent link to a website that contains simplified examples of the analysis."
"code",NA,NA,"vignetteDataLink","character","optional","NULL","base","https://github.com/cmerow/Merow_et_al_2016_GEB_Minxent_Examples","A permanent link to a website that contains data needed for demo code."
"code",NA,NA,"fullDataLink","character","optional","NULL","base","https://github.com/cmerow/Merow_et_al_2016_GEB_Minxent_Examples","A permanent link to a website that contains data needed for vignettes code."
"code",NA,NA,"codeNotes","character","no","NULL","base","https://github.com/cmerow/Merow_et_al_2016_GEB_Minxent_Examples","A permanent link to a website that contains data needed for full code."
