• # VCA

• Referenced in 1 article [sw16077]
• this package is to perform a variance component analysis (VCA) according to CLSI EP05 ... quadratic forms). The covariance matrix of variance components can be derived, which is used...
• # gtheory

• Referenced in 1 article [sw26191]
• Apply Generalizability Theory with R. Estimates variance components, generalizability coefficients, universe scores, and standard errors...
• # VARCLUS

• Referenced in 2 articles [sw14475]
• first principal component or the centroid component. The following are highlights of the VARCLUS procedure ... maximize the variance that is explained by the cluster components, summed over all the clusters...
• # CensMixReg

• Referenced in 12 articles [sw21090]
• depending on the structure of the mixture components. We develop an analytically simple, yet efficient ... rely on formulas for the mean and variance of the multivariate truncated Student-$t$ distributions...
• # getRDS

• Referenced in 6 articles [sw12595]
• results through simulation demonstrate that the software component produces an accurate point estimates ... M/M/1 queues compared to MT since its variance reduction is over 50 % in some cases...
• # NMath

• Referenced in 0 articles [sw31945]
• statistics library from CenterSpace Software provides numerical components for financial, engineering, and scientific applications ... regression, hypothesis tests, analysis of variance (ANOVA), principle component analysis, non-negative matrix factorization...
• # nsdr

• Referenced in 1 article [sw41791]
• methods. Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and Sliced Average Variance Estimation (SAVE...
• # hoggorm

• Referenced in 2 articles [sw29403]
• contains PCA (principal component analysis); PCR (principal component regression); PLSR (partial least squares regression): PLSR1 ... aims at understanding and interpretation of the variance in the data. hoggorm also also contains...
• # AugmentedPCA

• Referenced in 1 article [sw40917]
• improved downstream classification performance, produces principal components with greater class fidelity, and facilitates identification ... aligned with the principal axes of data variance with implications to development of specific types...