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matrixCorr

R-CMD-check.yaml test-coverage.yaml

matrixCorr computes correlation and related association matrices from small to high-dimensional data using simple, consistent functions and sensible defaults. It includes shrinkage and robust options for noisy or p ≥ n settings, plus convenient print/plot methods. Performance-critical paths are implemented in C++ with BLAS/OpenMP and memory-aware symmetric updates. The API accepts base matrices and data frames and returns standard R objects via a consistent S3 interface.

Supported measures include Pearson, Spearman, Kendall, distance correlation, partial correlation, and robust biweight mid-correlation; agreement tools cover Bland–Altman (two-method and repeated-measures) and Lin’s concordance correlation coefficient (including repeated-measures LMM/REML extensions).

Features

Installation

# Install from GitHub
# install.packages("devtools")
devtools::install_github("Prof-ThiagoOliveira/matrixCorr")

Example

Correlation matrices (Pearson, Spearman, Kendall)

library(matrixCorr)

set.seed(1)
X <- as.data.frame(matrix(rnorm(300 * 6), ncol = 6))
names(X) <- paste0("V", 1:6)

R_pear <- pearson_corr(X)
R_spr  <- spearman_rho(X)
R_ken  <- kendall_tau(X)

print(R_pear, digits = 2)
plot(R_spr)   # heatmap

Robust correlation (biweight mid-correlation)

set.seed(2)
Y <- X
# inject outliers
Y$V1[sample.int(nrow(Y), 8)] <- Y$V1[sample.int(nrow(Y), 8)] + 8

R_bicor <- biweight_mid_corr(Y)
print(R_bicor, digits = 2)

High-dimensional shrinkage correlation (\(p >> n\))

set.seed(3)
n <- 60; p <- 200
Xd <- matrix(rnorm(n * p), n, p)
colnames(Xd) <- paste0("G", seq_len(p))

R_shr <- schafer_corr(Xd)
print(R_shr, digits = 2, max_rows = 6, max_cols = 6)

Partial correlation matrix

R_part <- partial_correlation(X)
print(R_part, digits = 2)

Distance correlation matrix

R_dcor <- distance_corr(X)
print(R_dcor, digits = 2)

Agreement analyses

Two-method Bland–Altman

set.seed(4)
x <- rnorm(120, 100, 10)
y <- x + 0.5 + rnorm(120, 0, 8)

ba <- bland_altman(x, y)
print(ba)
plot(ba)

Repeated-measures Bland–Altman (pairwise matrix)

set.seed(5)
S <- 20; Tm <- 6
subj  <- rep(seq_len(S), each = Tm)
time  <- rep(seq_len(Tm), times = S)

true  <- rnorm(S, 50, 6)[subj] + (time - mean(time)) * 0.4
mA    <- true + rnorm(length(true), 0, 2)
mB    <- true + 1.0 + rnorm(length(true), 0, 2.2)
mC    <- 0.95 * true + rnorm(length(true), 0, 2.5)

dat <- rbind(
  data.frame(y = mA, subject = subj, method = "A", time = time),
  data.frame(y = mB, subject = subj, method = "B", time = time),
  data.frame(y = mC, subject = subj, method = "C", time = time)
)
dat$method <- factor(dat$method, levels = c("A","B","C"))

ba_rep <- bland_altman_repeated(
  data = dat, response = "y", subject = "subject",
  method = "method", time = "time",
  include_slope = FALSE, use_ar1 = FALSE
)
summary(ba_rep)
# plot(ba_rep)  # faceted BA scatter by pair

Two-method Lin’s concordance correlation

# Lin's CCC for x vs y (with CI + heatmap)
cc2 <- ccc(cbind(x = x, y = y), ci = TRUE)
print(cc2)
summary(cc2)
plot(cc2, title = "Lin's CCC (two methods)")

Lin’s concordance correlation coefficient (repeated-measures LMM/REML)

set.seed(6)
S <- 30; Tm <- 8
id     <- factor(rep(seq_len(S), each = 2 * Tm))
method <- factor(rep(rep(c("A","B"), each = Tm), times = S))
time   <- rep(rep(seq_len(Tm), times = 2), times = S)

u  <- rnorm(S, 0, 0.8)[as.integer(id)]
g  <- rnorm(S * Tm, 0, 0.5)
g  <- g[ (as.integer(id) - 1L) * Tm + as.integer(time) ]
y  <- (method == "B") * 0.3 + u + g + rnorm(length(id), 0, 0.7)

dat_ccc <- data.frame(y, id, method, time)

# Using non-parametric approch
ccc_rep_u <- ccc_pairwise_u_stat(
  data = dat_ccc, response = "y", method = "method", time = "time",
  ci = TRUE
)
print(ccc_rep_u)
summary(ccc_rep_u)
plot(ccc_rep_u, title = "Repeated-measures CCC (U-statistic)")

# Using LMM approch
fit_ccc <- ccc_lmm_reml(dat_ccc, response = "y", rind = "id",
                        method = "method", time = "time", ci = TRUE)
summary(fit_ccc)  # overall CCC, variance components, SEs/CI

Contributing

Issues and pull requests are welcome. Please see CONTRIBUTING.md for guidelines and cran-comments.md/DESCRIPTION for package metadata.

License

MIT Thiago de Paula Oliveira

See inst/LICENSE for the full MIT license text.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.