Author: Chia-Yi Chiu (Rutgers, the State University of New Jersey) and Wenchao
Ma (Rutgers, the State University of New Jersey)
Description: Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; <doi:10.1007/s11336-009-9125-0>). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

Title: Optimal Out-of-Sample Forecast Evaluation and Testing under
Description: Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>.
Imports: forecast, Matrix, methods, stats

Title: Acute Chronic Workload Ratio Calculation
Maintainer: Jorge R Fernandez-Santos <jorgedelrosario.fernandez@uca.es>
Description: Functions for calculating the acute chronic workload ratio using three
different methods: exponentially weighted moving average (EWMA), rolling
average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this
methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589>
for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.
Author: Jorge R Fernandez-Santos [aut, cre]

Title: Automated Deconvolution Augmentation of Profiles for Tissue
Maintainer: Samuel A Danziger <sam.danziger@gmail.com>
Description: Augments existing or de-novo cell-type signature matrices to deconvolve bulk gene expression data.
This package expands on the techniques outlined in Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y,
Hoang CD, Diehn M, Alizadeh, AA (2015) <doi:10.1038/nmeth.3337>'s Nature Methods paper:
'Robust enumeration of cell subsets from tissue expression profiles'  to allow a user to easily add
their own cell types (e.g. a tumor specific cell type) to Newman's LM22 or other signature matrix.
Imports: missForest, e1071, WGCNA, ComICS, pheatmap, doParallel,
quantmod, preprocessCore, pcaMethods, foreach, DeconRNASeq
Packaged: 2019-05-17 01:18:32 UTC; sdanziger