```{asis, echo = {{model_maxent_knit}}, eval = {{model_maxent_knit & !cat_envs_knit}}, include = {{model_maxent_knit & !cat_envs_knit}}}
Generating a species distribution model using the r '{{algMaxent_rmd}}'
algorithm as implemented in ENMeval V2.0 (with clamping = r {{clampSel_rmd}}
).
For tuning using r {{fcs_rmd}}
feature classes and regularization multipliers in the r {{rms_rmd}}
range increasing by r {{rmsStep_rmd}}
. Not using any categorical predictor variables.
```r}, include = {{model_maxent_knit & !cat_envs_knit}}} # Run maxent model for the selected species model_{{spAbr}} <- model_maxent( occs = occs_{{spAbr}}, bg = bgEnvsVals_{{spAbr}}, user.grp = groups_{{spAbr}}, bgMsk = bgMask_{{spAbr}}, rms = {{rms_rmd}}, rmsStep = {{rmsStep_rmd}}, fcs = {{fcs_rmd}}, clampSel = {{clampSel_rmd}}, algMaxent = "{{algMaxent_rmd}}", parallel = {{parallel_rmd}}, numCores = {{numCores_rmd}})
```{asis, echo = {{model_maxent_knit & cat_envs_knit}}, eval = {{model_maxent_knit & cat_envs_knit}}, include = {{model_maxent_knit & cat_envs_knit}}}
Generating a species distribution model using the r '{{algMaxent_rmd}}'
algorithm as implemented in ENMeval V2.0 (with clamping = r {{clampSel_rmd}}
).
For tuning using r {{fcs_rmd}}
feature classes and regularization multipliers in the r {{rms_rmd}}
range increasing by r {{rmsStep_rmd}}
. Using a total of r {{catEnvsNum_rmd}}
categorical predictor variables.
```r}, include = {{model_maxent_knit & cat_envs_knit}}} # Run maxent model for the selected species model_{{spAbr}} <- model_maxent( occs = occs_{{spAbr}}, bg = bgEnvsVals_{{spAbr}}, user.grp = groups_{{spAbr}}, bgMsk = bgMask_{{spAbr}}, rms = {{rms_rmd}}, rmsStep = {{rmsStep_rmd}}, fcs = {{fcs_rmd}}, clampSel = {{clampSel_rmd}}, algMaxent = "{{algMaxent_rmd}}", catEnvs = "{{catEnvs_rmd}}", parallel = {{parallel_rmd}}, numCores = {{numCores_rmd}})
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