Mandala for R

Purpose-built mixed model analysis for breeding programs, simplified for R.

Listo Agriculture License: MIT R Package

Get Started Request Download

Why Mandala?

Field Trial Focus

Purpose-built for agricultural experiments. Supports RCBD, alpha-lattice, row-column, and multi-environment trial designs out of the box.

Spatial Analysis

Account for spatial correlation in your field layouts with built-in AR1×AR1 and other spatial models. Improve accuracy without complex setup.

Simple Syntax

Clean, intuitive formula interface. Specify fixed and random effects clearly—no steep learning curve required.

Heritability & BLUPs

Unified heritability estimates (Cullis, Piepho methods) and easy extraction of BLUPs/BLUEs for selection decisions.

Quick Start

Request download from Listo Agriculture:

# Request a download at https://listoagriculture.com/products
# Then install from the file you receive:
install.packages("path/to/mandala_x.y.z.tar.gz", repos = NULL, type = "source")

Analyze a field trial in just a few lines:

library(mandala)

# Fit a Randomized Complete Block Design
model <- mandala(
  fixed  = yield ~ genotype,
  random = ~ block,
  data   = field_data
)

# View variance components
summary(model)$varcomp

# Get heritability estimates
h2 <- h2_estimates(random_mod = model_random, 
                   fixed_mod  = model, 
                   genotype   = "genotype")

# Extract BLUEs for selection
blues <- model$BLUEs

Supported Experimental Designs

RCBD

Randomized Complete Block Design with genotypes as fixed effects and blocks as random.

Alpha-Lattice

Incomplete block designs with nested blocking structure for large germplasm sets.

Row-Column

Resolvable designs accounting for both row and column effects in field layouts.

Multi-Environment

Analyze genotype × environment interactions across multiple locations and years.

Spatial Models

AR1×AR1 and other correlation structures to model field heterogeneity.

Split-Plot

Whole-plot and sub-plot factors with appropriate error structure.

Package Comparison

Wondering which package to use? Here’s how Mandala compares:

Feature Mandala Sommer lme4
Field trial focus ✓✓✓ ✓✓ ✓
Spatial models ✓✓✓ ✓✓✓ ○
Genomic selection ✓ ✓✓✓ ○
General purpose ✓ ✓✓ ✓✓✓
Ease of use ✓✓✓ ✓✓ ✓✓
Flexibility ✓✓ ✓✓✓ ✓✓✓

✓✓✓ = Excellent | ✓✓ = Good | ✓ = Basic | ○ = Limited

Use Mandala when:

  • Analyzing standard field trial designs
  • Need spatial correlation modeling
  • Routine variety testing & selection
  • Quick turnaround is important
  • Heritability estimation is needed

Consider alternatives when:

  • Complex genomic prediction models (→ sommer)
  • Multi-trait analysis (→ sommer)
  • General mixed models outside agriculture (→ lme4)
  • Need maximum flexibility (→ lme4/sommer)

Learn More

1

Introduction to Mandala

Installation, key features, and your first analysis

2

Working with Example Datasets

RCBD, alpha-lattice, and multi-environment trial examples

3

Comparison with Sommer

Side-by-side analysis for quantitative genetics applications

4

Comparison with lme4

When to use Mandala vs general-purpose mixed models

About

Mandala is developed by Listo Agriculture, a software company specializing in tools for plant and animal breeding programs.

NoteCitation

If you use Mandala in your research, please cite:

Bhoja Raj Basnet and Listo Agriculture (2025). mandala: Mixed Model Analysis for Agricultural Field Trials.
R package. https://listoagriculture.com/products

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