An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0.

This package provides a graphical user interface with an integrated diagrammer for latent variable models from the lavaan package. It offers two core functions: first, lavaangui() launches a web application that allows users to specify models by drawing path diagrams, fitting them, assessing model fit, and more; second, plot_lavaan() creates interactive path diagrams from models specified in lavaan'. Karch (2024) <doi: 10.1080/10705511.2024.2420678> contains a tutorial.

Read and write access to PNG image files using the LodePNG library. The package has no external dependencies.

Temporary and permanent message queues for R. Built on top of SQLite databases. SQLite provides locking, and makes it possible to detect crashed consumers. Crashed jobs can be automatically marked as "failed", or put in the queue again, potentially a limited number of times.

This package provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types.

It uses phenological and productivity-related variables derived from time series of vegetation indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description of the methods used in the package to calculate the indicator.

Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) <doi:10.18637/jss.v093.i07>.

This package provides likelihood functions as defined by Fisher (1922) <doi:10.1098/rsta.1922.0009> and a function that creates likelihood functions from density functions. The functions are meant to aid in education of likelihood based methods.

This package provides a set of functions and tools to conduct acoustic source localization, as well as organize and check localization data and results. The localization functions implement the modified steered response power algorithm described by Cobos et al. (2010) <doi:10.1109/LSP.2010.2091502>.

Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.

This package provides a simple progress bar showing estimated remaining time. Multiple forecast methods and user defined forecast method for the remaining time are supported.

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.

This package provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.

This package provides functions to sample from the double log normal distribution and calculate the density, distribution and quantile functions.

This package provides a set of streamlined functions that allow easy generation of linear regression diagnostic plots necessarily for checking linear model assumptions. This package is meant for easy scheming of linear regression diagnostics, while preserving merits of "The Grammar of Graphics" as implemented in ggplot2'. See the ggplot2 website for more information regarding the specific capability of graphics.

Implementation of the three-step approach of (latent transition) cognitive diagnosis model (CDM) with covariates. This approach can be used for single time-point situations (cross-sectional data) and multiple time-point situations (longitudinal data) to investigate how the covariates are associated with attribute mastery. For multiple time-point situations, the three-step approach of latent transition CDM with covariates allows researchers to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) <doi:10.3102/10769986231163320> and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) <doi:10.17632/kpjp3gnwbt.1>.

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values.

Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.

Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data by kernel-weighted estimating equation.

This package provides S3 classes to represent low rank matrix decompositions.

Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design.

This package provides a collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.

Network analysis usually requires estimating the uncertainty of graph statistics. Through this package, we provide tools to bootstrap various networks via local bootstrap procedure. Additionally, it includes functions for generating probability matrices, creating network adjacency matrices from probability matrices, and plotting network structures. The reference will be updated soon.