Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>, Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>, Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.

Efficient procedures for fitting conditional graphical lasso models that link a set of predictor variables to a set of response variables (or tasks), even when the response data may contain missing values. missoNet simultaneously estimates the predictor coefficients for all tasks by leveraging information from one another, in order to provide more accurate predictions in comparison to modeling them individually. Additionally, missoNet estimates the response network structure influenced by conditioning predictor variables using a L1-regularized conditional Gaussian graphical model. Unlike most penalized multi-task regression methods (e.g., MRCE), missoNet is capable of obtaining estimates even when the response data is corrupted by missing values. The method automatically enjoys the theoretical and computational benefits of convexity, and returns solutions that are comparable to the estimates obtained without missingness.

Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the MplusAutomation package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. âMplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.â Structural Equation Modeling, 25(4), 621â 638. <doi:10.1080/10705511.2017.1402334> The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). â An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.â Advances in Life Course Research, 43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.

Generate all necessary R/Rmd/shell files for data processing after running GGIR (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.

Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Returns almost all features that has been extracted from Position Specific Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) and 20 columns produced by PSI-BLAST which is a program to produce PSSM Matrix from multiple sequence alignment of proteins see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some of these features are described in Zahiri, J., et al.(2013) <DOI:10.1016/j.ygeno.2013.05.006>, Saini, H., et al.(2016) <DOI:10.17706/jsw.11.8.756-767>, Ding, S., et al.(2014) <DOI:10.1016/j.biochi.2013.09.013>, Cheng, C.W., et al.(2008) <DOI:10.1186/1471-2105-9-S12-S6>, Juan, E.Y., et al.(2009) <DOI:10.1109/CISIS.2009.194>.

colorout is an R package that colorizes R output when running in terminal emulator.

R STDOUT is parsed and numbers, negative numbers, dates in the standard format, strings, and R constants are identified and wrapped by special ANSI scape codes that are interpreted by terminal emulators as commands to colorize the output. R STDERR is also parsed to identify the expressions warning and error and their translations to many languages. If these expressions are found, the output is colorized accordingly; otherwise, it is colorized as STDERROR (blue, by default).

You can customize the colors according to your taste, guided by the color table made by the command show256Colors(). You can also set the colors to any arbitrary string. In this case, it is up to you to set valid values.

Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation and Review Technique) and LESS (Least Cost Estimating and Scheduling) methods. The package sets the critical path, schedule and Gantt chart. In addition, it allows to draw a graph even with marked critical activities. For more information about project management see: Taha H. A. "Operations Research. An Introduction" (2017, ISBN:978-1-292-16554-7), Rama Murthy P. "Operations Research" (2007, ISBN:978-81-224-2944-2), Yuval Cohen & Arik Sadeh (2006) "A New Approach for Constructing and Generating AOA Networks", Journal of Engineering, Computing and Architecture 1. 1-13, Konarzewska I., Jewczak M., Kucharski A. (2020, ISBN:978-83-8220-112-3), MiszczyÅ ska D., MiszczyÅ ski M. "Wybrane metody badaÅ operacyjnych" (2000, ISBN:83-907712-0-9).

Routines and tools for assessing the quality of content analysis on the basis of the Iota Reliability Concept. The concept is inspired by item response theory and can be applied to any kind of content analysis which uses a standardized coding scheme and discrete categories. It is also applicable for content analysis conducted by artificial intelligence. The package provides reliability measures for a complete scale as well as for every single category. Analysis of subgroup-invariance and error corrections are implemented. This information can support the development process of a coding scheme and allows a detailed inspection of the quality of the generated data. Equations and formulas working in this package are part of Berding et al. (2022)<doi:10.3389/feduc.2022.818365> and Berding and Pargmann (2022) <doi:10.30819/5581>.

The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>; - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>; - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>; - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.

High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).

Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is Gaussianize, which works similarly to scale, but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x MyFavoriteDistribution and use it in their analysis right away.

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.

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., Connection Weights described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like SmoothGrad described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, Gradient x Input or Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).

Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.

We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.

Principal Component Analysis (PCA) extracts the fundamental structure of the data without the need to build any model to represent it. This "summary" of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular; users can also identify an optimal number of principal components via different metrics, such as the elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

This package implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.

Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.

DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864.

Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments. The APCtools package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.

This package provides a collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as compositions', robCompositions', sp', gstat and RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at <https://tinyurl.com/UCLgeostats/>.

Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters.

Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. iNEXT.3D extends iNEXT to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) <doi:10.1111/2041-210X.13682> for more details.