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Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported.

Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors.