Home range estimation is an important analytical method; yet best practices for addressing the effects of spatial variation in detection probability on home range estimates remains elusive. We introduce the R package “DiagnoseHR,” simulation tools for assessing how variation in detection probability arising from landscape, animal behavior, and methodological processes affects home range inference. We demonstrate the utility of simulation methods for home range analysis planning by comparing bias arising from three home range estimation methods under multiple detection scenarios. We simulated correlated random walks in three landscapes that varied in detection probability and constructed home ranges from locations filtered through a range of sampling protocols. Home range estimates were less biased by reduced detection probability when sampling effort was increased, but the effects of sampling day distribution were marginal. Like others, we found that kernel density estimates were the least affected by variation in detection probability, while minimum convex polygons were most affected. Our results illustrate the value of quantifying uncertainty in home range estimates and suggest that field biologists working in conditions with low-detection may wish to weight sample-size greater than concerns about temporal autocorrelation when designing sampling protocols.