###################################################################################### ########################## DEEP AUTOENCODER NETWORK ########################## ########################## CONNECTED TO ########################## ########################## GEOGRAPHICAL RANDOM FOREST ########################## ###################################################################################### Authors: Zeinab SOLTANI & Saeid ESMAEILOGHLI December 10, 2023 -------------------------------------------------------------------------------------------------------------------------------------- Name of code: Deep autoencoder network connected to geographical random forest (DAN‒GRF). Title of paper: A deep autoencoder network connected to geographical random forest for spatially aware geochemical anomaly detection. Developers and contact details: Zeinab Soltani (e-mail: zs.soltani@aut.ac.ir) and Saeid Esmaeiloghli (e-mail: esmaeiloghli@gmail.com). Year first available: 2023. Hardware required: a computer with Intel(R) Core(TM) i5 @ 2.40 GHz, four cores, eight logical processors, RAM of 8.00 GB or higher. Software required: R 4.1.2 or higher. Program language: R language environment. Program size: 17.80 KB. The computer code with inset tutorial commands was deposited in a GitHub repository and is available at https://github.com/Saeid1986/DAN-GRF.git. Moreover, a user manual providing instructions on executing the program, small-sized test data, and relevant output results were deposited.