R (>= 4.0.5), GPvecchia,fields, mapproj, shape, codetools

R (>= 4.1.0), GPvecchia,fields, mapproj, shape, codetools

Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://api.mongodb.com/python/current/#> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.
MongoDB (>= 3.4.0), Python (>= 2.7). Installation
instructions and links can be found in the README file.

Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://github.com/mongodb/mongo-python-driver> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.

Calculating Geographic Range from Occurrence Data
Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.

Processed a various types of data on GEO and TCGA
person(given = "Erqiang", family = "Hu", email = "13766876214@163.com", role  = c("aut", "cre"))
Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA)
provide us with a wealth of data, such as RNA-seq, DNA Methylation,
and Copy number variation data. It's easy to download data from TCGA using the
gdc tool, but processing these data into a format suitable for bioinformatics
analysis requires more work. This R package was developed to handle these data.

Processing various types of data on GEO and TCGA

Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA)
provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP
and Copy number variation data. It's easy to download data from TCGA using the
gdc tool, but processing these data into a format suitable for bioinformatics
analysis requires more work. This R package was developed to handle these data.
knitr, rmarkdown, DESeq2, S4Vectors, ChAMP, impute, tidyr,
clusterProfiler, org.Hs.eg.db, edgeR, limma, quantreg