Compute Decision Interval and Average Run Length for CUSUM Charts

Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.

Crew Launcher Plugins for Traditional High-Performance Computing Clusters

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'crew.cluster' package extends the 'mirai'-powered 'crew' package with worker launcher plugins for traditional high-performance computing systems. Inspiration also comes from packages 'mirai' by Gao (2023) < https://github.com/r-lib/mirai>, 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) < https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischl, and Surmann (2017). .

Asymptotic Classification Theory for Cognitive Diagnosis

Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; ). 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.

'FASTA' ML and ‘altall’ Sequences from IQ-TREE .state Files

Takes a .state file generated by IQ-TREE as an input and, for each ancestral node present in the file, generates a FASTA-formatted maximum likelihood (ML) sequence as well as an ‘AltAll’ sequence in which uncertain sites, determined by the two parameters thres_1 and thres_2, have the maximum likelihood state swapped with the next most likely state as described in Geeta N. Eick, Jamie T. Bridgham, Douglas P. Anderson, Michael J. Harms, and Joseph W. Thornton (2017), "Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty" .

Efficient Determination of Sample Size in Balanced Design of Experiments

For a balanced design of experiments, this package calculates the sample size required to detect a certain standardized effect size, under a significance level. This package also provides three graphs; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power, which show the mutual relationship between the sample size, power and the detectable standardized effect size. The detailed procedure is described in R. V. Lenth (2006-9) < https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) , and Douglas C. Montgomery (2013, ISBN: 0849323312).

Data sets from Devore's "Prob and Stat for Eng (7th ed)"

Data sets and sample analyses from Jay L. Devore (2008), "Probability and Statistics for Engineering and the Sciences (7th ed)", Thomson.

Forest Yield Tables for Northwest Germany and their Application

The new yield tables developed by the Northwest German Forest Research Institute (NW-FVA) provide a forest management tool for the five main commercial tree species oak, beech, spruce, Douglas-fir and pine for northwestern Germany. The new method applied for deriving yield tables combines measurements of growth and yield trials with growth simulations using a state-of-the-art single-tree growth simulator. By doing so, the new yield tables reflect the current increment level and the recommended graduated thinning from above is the underlying management concept. The yield tables are provided along with methods for deriving the site index and for interpolating between age and site indices and extrapolating beyond age and site index ranges. The inter-/extrapolations are performed traditionally by the rule of proportion or with a functional approach.

Tree Taper Curves and Sorting Based on 'TapeR'

Providing new german-wide 'TapeR' Models and functions for their evaluation. Included are the most common tree species in Germany (Norway spruce, Scots pine, European larch, Douglas fir, Silver fir as well as European beech, Common/Sessile oak and Red oak). Many other species are mapped to them so that 36 tree species / groups can be processed. Single trees are defined by species code, one or multiple diameters in arbitrary measuring height and tree height. The functions then provide information on diameters along the stem, bark thickness, height of diameters, volume of the total or parts of the trunk and total and component above-ground biomass. It is also possible to calculate assortments from the taper curves. Uncertainty information is provided for diameter, volume and component biomass estimation.

Automatic Fixed Rank Kriging

Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) . For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes 'LatticeKrig' package.

Higher Order Likelihood Inference

Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).