ALUES

0.2.1

Agricultural Land Use Evaluation System

0packages depend

3.4Kdownloads / year

78.7%test coverage

13/13checks pass

Overview

About

Maintained by Al-Ahmadgaid B. AsaadFirst published 2022-01-121 releasesCRAN page ↗GitHub ↗

Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("ALUES", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("alstat/ALUES")

Development version from alstat/ALUES. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-alues

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-ALUES

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-ALUES

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 5.5 min · r-devel-linux-x86_64-fedora-clang

Code health

Yes

Tests · ratio 0.41

78.7%

Coverage · measured lines

100%

Documentation · exports

Dependencies · direct

Check history

OK2026-04-22
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-04-18
13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
OK2026-03-13
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-03-12
13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMEYes · 218 wordsVignettesYes · dynamicpkgdown siteNoNEWSNoCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 100%
Return-value docs: 100%
References docs: 0%

Downloads

3.4K

CRAN downloads in the past year

Rank #12,374 · ~9/day · ~281/mo

Releases R versions

Daily download trend is not available in this view yet.

31030 days

86390 days

3.4K1 year

Compare downloads with other packages →

Also on403 r2u53 autocran58 c2d4u

Repository

GitHub ↗ alstat/alues

16Stars

8Forks

1Open issues

0Open PRs

1Releases

211Commits

6Contributors

rr-packageagricultureagricultural-modelling

211 commits · Last activity 2022-06-11 · 0% stars, 30d

Stars over time

2024-10-31 · 102026-07-07 · 16

Repository practices

Upstream repositoryBeta

2 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/alstat/alues on 2026-07-19.

Continuous integration (1)

GitHub Actions

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

9 external dependencies (excludes base and recommended)

Depends (2)

R >= 3.5.0Rcpp

Imports (0)

none

LinkingTo (1)

Rcpp

Suggests (7)

testthat markdown knitr microbenchmark ggmap raster reshape2

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

Code Composition

R 9,528 (53%)Rd 7,085 (39%)C++ 774 (4%)Vignettes 689 (4%)

Code characteristics

Cyclomatic complexity: 1.0 median / 134 max
Test cases: 404 / 0.41 per code line

Test coverage

Line coverage

79%

Expression

77.1%

Tests / Examples

78.7% / 30% ex

Functions

19 2 exported

Complexity

24.6 avg / 134 max

Call network

19 nodes / 15 edges

Call graph

Open call graph →

Loading call graph…

Lowest coverage

19 functions

Function	Cyclo	Coverage
suitability	134	74%
suit exp	31	79%
overall_suit exp	27	84%

Datasets

Bundled datasets · 230

Name	Class	Rows × Cols	Also ships in
ALFALFASoil	data.frame	12 × 8	–
ALFALFATemp	data.frame	1 × 8	–
ALFALFATerrain	data.frame	6 × 8	–
ALFALFAWater	data.frame	3 × 8	–
AVOCADOSoil	data.frame	10 × 8	–
AVOCADOTemp	data.frame	2 × 8	–
AVOCADOTerrain	data.frame	5 × 8	–
AVOCADOWater	data.frame	2 × 8	–
BAMBOOSoil	data.frame	4 × 8	–
BAMBOOTemp	data.frame	1 × 8	–
BAMBOOTerrain	data.frame	1 × 8	–
BAMBOOWater	data.frame	1 × 8	–
BANANASoil	data.frame	12 × 8	–
BANANATemp	data.frame	3 × 8	–
BANANATerrain	data.frame	6 × 8	–
BANANAWater	data.frame	2 × 8	–
BARLEYSoil	data.frame	13 × 8	–
BARLEYTemp	data.frame	3 × 8	–
BARLEYTerrain	data.frame	6 × 8	–
BARLEYWater	data.frame	3 × 8	–
BEANCASoil	data.frame	9 × 8	–
BEANCATemp	data.frame	2 × 8	–
BEANCATerrain	data.frame	6 × 8	–
BEANCAWater	data.frame	2 × 8	–
BEANSSoil	data.frame	12 × 8	–
BEANSTemp	data.frame	2 × 8	–
BEANSTerrain	data.frame	7 × 8	–
BEANSWater	data.frame	5 × 8	–
CABBAGESoil	data.frame	12 × 8	–
CABBAGETemp	data.frame	3 × 8	–
CABBAGETerrain	data.frame	7 × 8	–
CABBAGEWater	data.frame	2 × 8	–
CARROTSSoil	data.frame	11 × 8	–
CARROTSTemp	data.frame	2 × 8	–
CARROTSTerrain	data.frame	6 × 8	–
CARROTSWater	data.frame	2 × 8	–
CASHEWSoil	data.frame	8 × 8	–
CASHEWTemp	data.frame	2 × 8	–
CASHEWTerrain	data.frame	8 × 8	–
CASHEWWater	data.frame	2 × 8	–
CASSAVASoil	data.frame	12 × 8	–
CASSAVATemp	data.frame	3 × 8	–
CASSAVATerrain	data.frame	6 × 8	–
CASSAVAWater	data.frame	3 × 8	–
CHICKPEASoil	data.frame	11 × 8	–
CHICKPEATemp	data.frame	1 × 8	–
CHICKPEATerrain	data.frame	8 × 8	–
CHICKPEAWater	data.frame	3 × 8	–
CINNAMONSoil	data.frame	9 × 8	–
CINNAMONTemp	data.frame	1 × 8	–
CINNAMONTerrain	data.frame	2 × 8	–
CINNAMONWater	data.frame	2 × 8	–
CITRUSSoil	data.frame	12 × 8	–
CITRUSTemp	data.frame	8 × 8	–
CITRUSTerrain	data.frame	6 × 8	–
CITRUSWater	data.frame	5 × 8	–
COCOASoil	data.frame	10 × 8	–
COCOATemp	data.frame	3 × 8	–
COCOATerrain	data.frame	5 × 8	–
COCOAWater	data.frame	4 × 8	–
COCONUTSoil	data.frame	6 × 8	–
COCONUTTemp	data.frame	1 × 8	–
COCONUTTerrain	data.frame	5 × 8	–
COCONUTWater	data.frame	3 × 8	–
COFFEEARSoil	data.frame	11 × 8	–
COFFEEARTemp	data.frame	3 × 8	–
COFFEEARTerrain	data.frame	6 × 8	–
COFFEEARWater	data.frame	4 × 8	–
COFFEEROSoil	data.frame	10 × 8	–
COFFEEROTemp	data.frame	3 × 8	–
COFFEEROTerrain	data.frame	5 × 8	–
COFFEEROWater	data.frame	4 × 8	–
COTTONSoil	data.frame	13 × 8	–
COTTONTemp	data.frame	6 × 8	–
COTTONTerrain	data.frame	7 × 8	–
COTTONWater	data.frame	6 × 8	–
COWPEASoil	data.frame	11 × 8	–
COWPEATemp	data.frame	3 × 8	–
COWPEATerrain	data.frame	7 × 8	–
COWPEAWater	data.frame	6 × 8	–
CUCUMBERSoil	data.frame	10 × 8	–
CUCUMBERTemp	data.frame	1 × 8	–
CUCUMBERTerrain	data.frame	7 × 8	–
CUCUMBERWater	data.frame	2 × 8	–
GROUNDNUTSSoil	data.frame	15 × 8	–
GROUNDNUTSTemp	data.frame	3 × 8	–
GROUNDNUTSTerrain	data.frame	7 × 8	–
GROUNDNUTSWater	data.frame	6 × 8	–
GUAVASoil	data.frame	9 × 8	–
GUAVATemp	data.frame	1 × 8	–
GUAVATerrain	data.frame	5 × 8	–
GUAVAWater	data.frame	1 × 8	–
LONGANSoil	data.frame	12 × 8	–
LONGANTemp	data.frame	3 × 8	–
LONGANTerrain	data.frame	6 × 8	–
LONGANWater	data.frame	1 × 8	–
LaoCaiLT	data.frame	2,928 × 10	–
LaoCaiTemp	data.frame	2,928 × 12	–
LaoCaiWater	data.frame	2,928 × 13	–
MAIZESoil	data.frame	15 × 8	–
MAIZETemp	data.frame	2 × 8	–
MAIZETerrain	data.frame	7 × 8	–
MAIZEWater	data.frame	9 × 8	–
MANGOSoil	data.frame	12 × 8	–
MANGOTemp	data.frame	2 × 8	–
MANGOTerrain	data.frame	6 × 8	–
MANGOWater	data.frame	5 × 8	–
MILLETSSoil	data.frame	11 × 8	–
MILLETSTemp	data.frame	3 × 8	–
MILLETSTerrain	data.frame	6 × 8	–
MILLETSWater	data.frame	5 × 8	–
MarinduqueLT	data.frame	881 × 6	–
MarinduqueTemp	data.frame	881 × 14	–
MarinduqueWater	data.frame	881 × 14	–
OILPALMSoil	data.frame	10 × 8	–
OILPALMTemp	data.frame	3 × 8	–
OILPALMTerrain	data.frame	4 × 8	–
OILPALMWater	data.frame	3 × 8	–
OLIVESSoil	data.frame	10 × 8	–
OLIVESTemp	data.frame	2 × 8	–
OLIVESTerrain	data.frame	6 × 8	–
OLIVESWater	data.frame	3 × 8	–
ONIONSoil	data.frame	11 × 8	–
ONIONTemp	data.frame	2 × 8	–
ONIONTerrain	data.frame	6 × 8	–
ONIONWater	data.frame	2 × 8	–
PAPAYASoil	data.frame	9 × 8	–
PAPAYATemp	data.frame	2 × 8	–
PAPAYATerrain	data.frame	1 × 8	–
PAPAYAWater	data.frame	3 × 8	–
PEACHSoil	data.frame	12 × 8	–
PEACHTemp	data.frame	1 × 8	–
PEACHTerrain	data.frame	6 × 8	–
PEACHWater	data.frame	1 × 8	–
PEARSoil	data.frame	12 × 8	–
PEARTemp	data.frame	1 × 8	–
PEARTerrain	data.frame	6 × 8	–
PEARWater	data.frame	1 × 8	–
PEASoil	data.frame	11 × 8	–
PEATemp	data.frame	2 × 8	–
PEATerrain	data.frame	7 × 8	–
PEAWater	data.frame	1 × 8	–
PEPPERGRSoil	data.frame	11 × 8	–
PEPPERGRTemp	data.frame	2 × 8	–
PEPPERGRTerrain	data.frame	7 × 8	–
PEPPERGRWater	data.frame	1 × 8	–
PERSIMMONSoil	data.frame	12 × 8	–
PERSIMMONTemp	data.frame	1 × 8	–
PERSIMMONTerrain	data.frame	6 × 8	–
PERSIMMONWater	data.frame	1 × 8	–
PINEAPPLESoil	data.frame	12 × 8	–
PINEAPPLETemp	data.frame	1 × 8	–
PINEAPPLETerrain	data.frame	7 × 8	–
PINEAPPLEWater	data.frame	2 × 8	–
PLUMSoil	data.frame	12 × 8	–
PLUMTemp	data.frame	1 × 8	–
PLUMTerrain	data.frame	6 × 8	–
PLUMWater	data.frame	1 × 8	–
POTATOSWSoil	data.frame	12 × 8	–
POTATOSWTemp	data.frame	2 × 8	–
POTATOSWTerrain	data.frame	7 × 8	–
POTATOSWWater	data.frame	5 × 8	–
POTATOSoil	data.frame	12 × 8	–
POTATOTemp	data.frame	4 × 8	–
POTATOTerrain	data.frame	7 × 8	–
POTATOWater	data.frame	5 × 8	–
RICEBRSoil	data.frame	6 × 8	–
RICEBRTemp	data.frame	4 × 8	–
RICEBRTerrain	data.frame	3 × 8	–
RICEBRWater	data.frame	7 × 8	–
RICEIWSoil	data.frame	12 × 8	–
RICEIWTemp	data.frame	4 × 8	–
RICEIWTerrain	data.frame	4 × 8	–
RICEIWWater	data.frame	7 × 8	–
RICENFSoil	data.frame	11 × 8	–
RICENFTemp	data.frame	4 × 8	–
RICENFTerrain	data.frame	2 × 8	–
RICENFWater	data.frame	7 × 8	–
RICEURSoil	data.frame	8 × 8	–
RICEURTemp	data.frame	4 × 8	–
RICEURTerrain	data.frame	6 × 8	–
RICEURWater	data.frame	7 × 8	–
RUBBERSoil	data.frame	10 × 8	–
RUBBERTemp	data.frame	3 × 8	–
RUBBERTerrain	data.frame	4 × 8	–
RUBBERWater	data.frame	3 × 8	–
SAFFLOWERSoil	data.frame	8 × 8	–
SAFFLOWERTemp	data.frame	2 × 8	–
SAFFLOWERTerrain	data.frame	6 × 8	–
SAFFLOWERWater	data.frame	6 × 8	–
SESAMESoil	data.frame	7 × 8	–
SESAMETemp	data.frame	3 × 8	–
SESAMETerrain	data.frame	6 × 8	–
SESAMEWater	data.frame	4 × 8	–
SORGHUMSoil	data.frame	12 × 8	–
SORGHUMTemp	data.frame	3 × 8	–
SORGHUMTerrain	data.frame	6 × 8	–
SORGHUMWater	data.frame	3 × 8	–
SOYASoil	data.frame	11 × 8	–
SOYATemp	data.frame	2 × 8	–
SOYATerrain	data.frame	7 × 8	–
SOYAWater	data.frame	9 × 8	–
SUGARCANESoil	data.frame	15 × 8	–
SUGARCANETemp	data.frame	4 × 8	–
SUGARCANETerrain	data.frame	7 × 8	–
SUGARCANEWater	data.frame	4 × 8	–
SUNFLOWERSoil	data.frame	11 × 8	–
SUNFLOWERTemp	data.frame	1 × 8	–
SUNFLOWERTerrain	data.frame	6 × 8	–
SUNFLOWERWater	data.frame	6 × 8	–
TEASoil	data.frame	12 × 8	–
TEATemp	data.frame	5 × 8	–
TEATerrain	data.frame	4 × 8	–
TEAWater	data.frame	4 × 8	–
TOBACCOSoil	data.frame	12 × 8	–
TOBACCOTemp	data.frame	1 × 8	–
TOBACCOTerrain	data.frame	7 × 8	–
TOBACCOWater	data.frame	3 × 8	–
TOMATOSoil	data.frame	12 × 8	–
TOMATOTemp	data.frame	4 × 8	–
TOMATOTerrain	data.frame	7 × 8	–
TOMATOWater	data.frame	2 × 8	–
WATERMELONSoil	data.frame	10 × 8	–
WATERMELONTemp	data.frame	1 × 8	–
WATERMELONTerrain	data.frame	7 × 8	–
WATERMELONWater	data.frame	2 × 8	–
WHEATSoil	data.frame	14 × 8	–
WHEATTemp	data.frame	4 × 8	–
WHEATTerrain	data.frame	6 × 8	–
WHEATWater	data.frame	4 × 8	–

People & History

People (3)

Maintainer (1)

Al-Ahmadgaid B. Asaad

Author, Maintainer

Authors (3)

Al-Ahmadgaid B. Asaad

Author, Maintainer

Arnold R. Salvacion

Author

Bui Tan Yen

Author

Package Timeline

1 release. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
0.2.1Latest
2026-03-10 · current release
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2022-01-12
Total releases: 1 / 4 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 3.5.0
Bundled data: 172 KB / 230 files
Download size: 2.7 MB
Installed size: not tracked yet
With dependencies: not tracked yet

Appears in task views

Agricultural Science

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