Package: SpiecEasi 2.1.1

Zachary Kurtz

SpiecEasi: Sparse Inverse Covariance for Ecological Statistical Inference

Estimate networks from the precision matrix of compositional microbial abundance data.

Authors:Zachary Kurtz [aut, cre], Christian Mueller [aut], Emily Miraldi [aut], Richard Bonneau [aut], Laura Tipton [ctb]

SpiecEasi_2.1.1.tar.gz
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SpiecEasi_2.1.1.tgz(r-4.6-x86_64)SpiecEasi_2.1.1.tgz(r-4.6-arm64)SpiecEasi_2.1.1.tgz(r-4.5-x86_64)SpiecEasi_2.1.1.tgz(r-4.5-arm64)
SpiecEasi_2.1.1.tar.gz(r-4.7-arm64)SpiecEasi_2.1.1.tar.gz(r-4.7-x86_64)SpiecEasi_2.1.1.tar.gz(r-4.6-arm64)SpiecEasi_2.1.1.tar.gz(r-4.6-x86_64)
SpiecEasi_2.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SpiecEasi/json (API)

# Install 'SpiecEasi' in R:

install.packages('SpiecEasi', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zdk123/spieceasi/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

amgut1.filt - American Gut Project
amgut2.filt.phy - American Gut Project
hmp216S - Human Microbiome Project 2
hmp2prot - Human Microbiome Project 2

On BioConductor:SpiecEasi-2.1.1(bioc 3.24)SpiecEasi-2.0.0(bioc 3.23)

software microbiome metagenomics graphandnetwork networkinference openblas cpp

9.89 score 235 stars 794 scripts 710 downloads 50 exports 63 dependencies

Last updated from:3c747724ed. Checks:1 WARNING, 9 OK, 6 FAIL. Indexed: yes.

The latest version of this package failed to build. Look at thebuild logs for more information.

Target	Result	Time	Files	Syslog
bioc-checks	WARNING	225
linux-devel-arm64	OK	673
linux-devel-x86_64	OK	682
source / vignettes	OK	277
linux-release-arm64	OK	662
linux-release-x86_64	OK	1064
macos-release-arm64	FAIL	96
macos-release-x86_64	FAIL	210
macos-oldrel-arm64	FAIL	101
macos-oldrel-x86_64	FAIL	184
windows-devel-arm64	OK	732
windows-devel-x86_64	OK	537
windows-release-arm64	OK	786
windows-release-x86_64	FAIL	65
windows-oldrel-x86_64	FAIL	63
wasm-release	OK	174

Exports:adj2igraph alr clr coat cor2cov cov2prec ebic edge.diss fitdistr get_comm_params getOptBeta getOptCov getOptiCov getOptInd getOptLambda getOptMerge getOptNet getRefit getStability graph2prec make_graph multi.spiec.easi neff neighborhood.net norm_pseudo norm_rdiric norm_to_total prec2cov pval.sparccboot qqdplot_comm rmvnegbin rmvnorm rmvpois rmvzinegbin rmvzipois robustPCA rzipois shannon sparcc sparccboot sparseiCov sparseLowRankiCov spiec.easi stars.pr stars.roc symBeta synth_comm_from_counts tril triu triu2diag

Dependencies:ade4 ape Biobase BiocGenerics biomformat Biostrings cli cluster codetools cpp11 crayon data.table digest farver foreach generics ggplot2 glmnet glue gtable huge igraph IRanges isoband iterators jsonlite labeling lattice lifecycle magrittr MASS Matrix mgcv multtest nlme permute phyloseq pixmap pkgconfig plyr pulsar R6 RColorBrewer Rcpp RcppArmadillo RcppEigen reshape2 rlang S4Vectors S7 scales Seqinfo shape sp stringi stringr survival vctrs vegan VGAM viridisLite withr XVector

Cross Domain SPIEC-EASI

Key features of cross-domain analysis | Example with custom data | Interpretation

Rendered fromcross-domain-interactions.Rmdusingknitr::rmarkdown

Last update: 2026-04-29
Started: 2025-09-24

Introduction to SpiecEasi

Installation | Available vignettes | Basic Usage | Analysis of American Gut data | Next steps

Rendered fromSpiecEasi.Rmdusingknitr::rmarkdown

Last update: 2026-04-29
Started: 2025-09-24

Learning latent variable graphical models

Key differences from standard SPIEC-EASI

Rendered fromlatent-variable-models.Rmdusingknitr::rmarkdown

Last update: 2026-04-29
Started: 2025-09-24

pulsar: parallel utilities for model selection

Windows-specific considerations | Option 1: Use batch mode with snow | Option 2: Use serial processing | Option 3: Use batch mode | Batch Mode | Performance comparison | Key parameters | Platform-specific recommendations | Unix-like systems (Linux, macOS): | Windows systems:

Rendered frompulsar-parallel.Rmdusingknitr::rmarkdown

Last update: 2026-04-29
Started: 2025-09-24

Troubleshooting

Common issues and solutions | 1. Empty networks | 2. Very dense networks | 3. Computational issues | 4. Windows parallel processing issues | 5. Convergence issues | 6. Memory issues | Platform-specific considerations | Windows users: | Unix-like systems (Linux, macOS): | Diagnostic functions | Parameter tuning guidelines | For small datasets (< 100 samples, < 50 taxa): | For medium datasets (100-1000 samples, 50-200 taxa): | For large datasets (> 1000 samples, > 200 taxa): | Windows-specific recommendations:

Rendered fromtroubleshooting.Rmdusingknitr::rmarkdown

Last update: 2026-04-29
Started: 2025-09-24

Working with phyloseq

Rendered fromphyloseq-integration.Rmdusingknitr::rmarkdown

Last update: 2026-04-29
Started: 2025-09-24

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Adjacency to igraph	adj2igraph
American Gut Project	AGP amgut1.filt amgut2.filt.phy
Additive log-ratio functions	alr alr.data.frame alr.default alr.matrix
s3 method for graph to other data types	as.data.frame.graph
s3 method for graph to other data types	as.matrix.graph
Centered log-ratio functions	clr clr.data.frame clr.default clr.matrix
COAT	coat
Convert a symmetric correlation matrix to a covariance matrix given the standard deviation	cor2cov
Covariance matrix to its matrix inverse (Precision matrix)	cov2prec
Extended BIC	ebic
Edge set dissimilarity	edge.diss
Fit parameters of a marginal distribution to some data vector	fitdistr
Get the parameters for the OTUs (along mar) of each community	get_comm_params
get StARS-optimal network	getOptBeta getOptCov getOptiCov getOptInd getOptLambda getOptMerge getOptNet getRefit getStability
Convert a symmetric graph (extension of R matrix class)	graph2prec
Human Microbiome Project 2	hmp2 hmp216S hmp2prot
Procedure to generate graph topologies for Gaussian Graphical Models	make_graph
multi domain SPIEC-EASI	multi.spiec.easi spiec.easi.list
N_effective: Compute the exponential of the shannon entropy. linearizes shannon entropy, for a better diveristy metric (effective number of species)	neff
Neighborhood net estimates	neighborhood.net
Normalize w/ Pseudocount	norm_pseudo
Normalize via dirichlet sampling	norm_rdiric
Total Sum Normalize	norm_to_total
Precision matrix (inverse covariance) to a covariance matrix	prec2cov
pulsar params	pulsar.params
SparCC p-vals	pval.sparccboot
qq-plot for theoretical vs observed communities	qqdplot_comm
Generate multivariate, Zero-inflated negative binomial data, with counts approximately correlated according to Sigma	rmvnegbin
Draw samples from multivariate, correlated normal distribution with counts correlated according to Sigma	rmvnorm
Generate multivariate poisson data, with counts approximately correlated according to Sigma	rmvpois
Generate multivariate, negative binomial data, with counts approximately correlated according to Sigma	rmvzinegbin
Generate multivariate, Zero-inflated poisson data, with counts approximately correlated according to Sigma	rmvzipois
robust PCA	robustPCA
Draw samples from a zero-inflated poisson distribution	rzipois
compute the shannon entropy from a vector (normalized internally)	shannon
sparcc wrapper	sparcc
Bootstrap SparCC	sparccboot
Sparse/penalized estimators of covariance matrices	sparseiCov
Sparse plus Low Rank inverse covariance	sparseLowRankiCov
SPIEC-EASI pipeline	spiec.easi spiec.easi.default spiec.easi.otu_table spiec.easi.phyloseq
stars.roc, stars.pr	stars.pr stars.roc
sym beta	symBeta
synth_comm_from_counts	synth_comm_from_counts

Usage by other packages (reverse dependencies)