This package provides functions for identifying, fitting, and applying continuous-space, continuous-time stochastic-process movement models to animal tracking data. The package is described in Calabrese et al (2016) <doi:10.1111/2041-210X.12559>, with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) <doi:10.1086/675504>, Fleming et al (2014) <doi:10.1111/2041-210X.12176>, Fleming et al (2015) <doi:10.1103/PhysRevE.91.032107>, Fleming et al (2015) <doi:10.1890/14-2010.1>, Fleming et al (2016) <doi:10.1890/15-1607>, Péron & Fleming et al (2016) <doi:10.1186/s40462-016-0084-7>, Fleming & Calabrese (2017) <doi:10.1111/2041-210X.12673>, Péron et al (2017) <doi:10.1002/ecm.1260>, Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>, Fleming et al (2018) <doi:10.1002/eap.1704>, Winner & Noonan et al (2018) <doi:10.1111/2041-210X.13027>, Fleming et al (2019) <doi:10.1111/2041-210X.13270>, Noonan & Fleming et al (2019) <doi:10.1186/s40462-019-0177-1>, Fleming et al (2020) <doi:10.1101/2020.06.12.130195>, Noonan et al (2021) <doi:10.1111/2041-210X.13597>, Fleming et al (2022) <doi:10.1111/2041-210X.13815>, Silva et al (2022) <doi:10.1111/2041-210X.13786>, Alston & Fleming et al (2023) <doi:10.1111/2041-210X.14025>.

Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, Blair and Imai (2012) <doi:10.1093/pan/mpr048>, Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>, Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.

Restic is a program that does backups right and was designed with the following principles in mind:

• Easy: Doing backups should be a frictionless process, otherwise you might be tempted to skip it. Restic should be easy to configure and use, so that, in the event of a data loss, you can just restore it. Likewise, restoring data should not be complicated.

• Fast: Backing up your data with restic should only be limited by your network or hard disk bandwidth so that you can backup your files every day. Nobody does backups if it takes too much time. Restoring backups should only transfer data that is needed for the files that are to be restored, so that this process is also fast.

• Verifiable: Much more important than backup is restore, so restic enables you to easily verify that all data can be restored.

• Secure: Restic uses cryptography to guarantee confidentiality and integrity of your data. The location the backup data is stored is assumed not to be a trusted environment (e.g. a shared space where others like system administrators are able to access your backups). Restic is built to secure your data against such attackers.

• Efficient: With the growth of data, additional snapshots should only take the storage of the actual increment. Even more, duplicate data should be de-duplicated before it is actually written to the storage back end to save precious backup space.

This package provides tools for extreme value modeling based on the r-largest order statistics framework. The package provides functions for parameter estimation via maximum likelihood, return level estimation with standard errors, profile likelihood-based confidence intervals, random sample generation, and entropy difference tests for selecting the number of order statistics r. Several r-largest order statistics models are implemented, including the four-parameter kappa (rK4D), generalized logistic (rGLO), generalized Gumbel (rGGD), logistic (rLD), and Gumbel (rGD) distributions. The rK4D methodology is described in Shin et al. (2022) <doi:10.1016/j.wace.2022.100533>, the rGLO model in Shin and Park (2024) <doi:10.1007/s00477-023-02642-7>, and the rGGD model in Shin and Park (2025) <doi:10.1038/s41598-024-83273-y>. The underlying distributions are related to the kappa distribution of Hosking (1994) <doi:10.1017/CBO9780511529443>, the generalized logistic distribution discussed by Ahmad et al. (1988) <doi:10.1016/0022-1694(88)90015-7>, and the generalized Gumbel distribution of Jeong et al. (2014) <doi:10.1007/s00477-014-0865-8>. Penalized likelihood approaches for extreme value estimation follow Martins and Stedinger (2000) <doi:10.1029/1999WR900330> and Coles and Dixon (1999) <doi:10.1023/A:1009905222644>. Selection of r is supported using methods discussed in Bader et al. (2017) <doi:10.1007/s11222-016-9697-3>. The package is intended for hydrological, climatological, and environmental extreme value analysis.

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

Restic is a program that does backups right and was designed with the following principles in mind:

• Easy: Doing backups should be a frictionless process, otherwise you might be tempted to skip it. Restic should be easy to configure and use, so that, in the event of a data loss, you can just restore it. Likewise, restoring data should not be complicated.

• Fast: Backing up your data with restic should only be limited by your network or hard disk bandwidth so that you can backup your files every day. Nobody does backups if it takes too much time. Restoring backups should only transfer data that is needed for the files that are to be restored, so that this process is also fast.

• Verifiable: Much more important than backup is restore, so restic enables you to easily verify that all data can be restored.

• Secure: Restic uses cryptography to guarantee confidentiality and integrity of your data. The location the backup data is stored is assumed not to be a trusted environment (e.g. a shared space where others like system administrators are able to access your backups). Restic is built to secure your data against such attackers.

• Efficient: With the growth of data, additional snapshots should only take the storage of the actual increment. Even more, duplicate data should be de-duplicated before it is actually written to the storage back end to save precious backup space.

The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function.

Markov chain Monte Carlo samplers for Bayesian quantile regression, based on the asymmetric Laplace distribution and the location-scale mixture representation of Kozumi and Kobayashi (2011) <doi:10.1080/00949655.2010.496117>. A binary response and an observed continuous response are both supported, each with three penalty layers behind one interface: no penalty, following Benoit and Van den Poel (2012) <doi:10.1002/jae.1216>; the Bayesian lasso, following Benoit, Al-Hamzawi and Yu (2013) <doi:10.1007/s00180-013-0439-0>; and the Bayesian adaptive lasso of Rubio Garcia (2023) <https://soar.wichita.edu/entities/publication/a2f86232-4704-4ec2-b685-751e7b04ec42>. In the binary family each is available as published and in a corrected form, the default, in which every improper prior component is replaced by a proper one so that the posterior exists unconditionally; the continuous family ships the corrected form only. The continuous adaptive-lasso layer at its default reproduces the penalty of Alhamzawi, Yu and Benoit (2012) <doi:10.1177/1471082X1101200304>. A binary threshold model identifies the coefficient vector only up to a positive scale, so the binary samplers expose the identification anchor as an explicit argument, allowing fixing the scale of the error distribution, fixing a single coefficient, and constraining the norm of the coefficient vector to be compared directly; an observed response identifies the scale, so the continuous samplers have no anchor and draw it every sweep. The MCMC cores are written in Fortran and called from R.

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: <https://www.nature.com/articles/s41467-019-10802-z.pdf> for more information about the development of DWLS and the methods behind our functions.

Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at <https://raw.githubusercontent.com/prdm0/hcci/master/references/dissertacao_mestrado.pdf>. The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) <doi:10.1080/00949650801935327>, while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) <doi:10.2307/2336685>. The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) <doi:10.1214/aos/1176350142>. This bootstrap scheme is known as weighted or wild bootstrap.

This package provides a comprehensive, generalized framework for computing, estimating, and validating Generalized Process Capability Indices (GPCIs). Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), survival functions (SF), and quantile functions with uncensored data parameter estimation via Maximum Likelihood Estimation (MLE). Provides classical and non-normal capability indices, including Cpy (Maiti, Saha and Nanda, 2010) <doi:10.1080/16843703.2010.11673233>, Spmk (Dey and Saha, 2019) <doi:10.1007/s41872-019-00081-4>, CpTk (Saha, Dey and Maiti, 2019) <doi:10.1007/s13198-019-00789-7>, Cpc (Saha, Dey and Nadarajah, 2022) <doi:10.1080/02664763.2021.1971632>, CNpmc (Alotaibi, Dey and Saha, 2022) <doi:10.1155/2022/3135264>, CNpmkc (Saha, Tripathi and Dey, 2024) <doi:10.1142/S021853932450013X>, CNpk (Saha, Dey and Maiti, 2018) <doi:10.1080/21681015.2018.1437793>, and Vannman capability indices. Computes parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% confidence levels using percentile, normal, basic, BCa, BCp, and studentized bootstrap methods. Evaluates Highest Posterior Density (HPD) intervals and Heidelberger-Welch convergence diagnostics. References: Maiti, Saha and Nanda (2010) <doi:10.1080/16843703.2010.11673233>, Saha, Dey and Maiti (2018) <doi:10.1080/21681015.2018.1437793>, Dey and Saha (2019) <doi:10.1007/s41872-019-00081-4>, Saha, Dey and Maiti (2019) <doi:10.1007/s13198-019-00789-7>, Alotaibi, Dey and Saha (2022) <doi:10.1155/2022/3135264>, Saha, Dey and Nadarajah (2022) <doi:10.1080/02664763.2021.1971632>, Saha, Tripathi and Dey (2024) <doi:10.1142/S021853932450013X>.

This package provides diagnostic functions for integrating longitudinal soil, plant and microbial observations during redox disturbance and recovery. Functions calculate stoichiometric potential oxygen demand, accessible electron capacity from explicitly supplied inventories and kinetic parameters, recovery signatures, fixed-reference domain scores, and exploratory multiblock scores with observation-coverage diagnostics. Memory is represented as a holobiont state accumulating from mineralogical, plant-acclimation and microbial-community legacies. An illustrative simulator produces closed Fe and Mn inventories alongside synthetic observations; its carbon, nitrogen, sulfur and oxygen budgets are not closed and its parameters are not calibrated to field rates. Simulation benchmarks assess agreement with a prescribed synthetic target and do not constitute empirical validation or parameter identification. Accuracy assessment is cluster-aware: intervals come from resampling whole trajectories, agreement is reported as Lin's concordance coefficient alongside correlation, and mean squared error is partitioned into bias, variance mismatch and lack of correlation. The measured quantities follow Sander, Hofstetter and Gorski (2015) <doi:10.1021/acs.est.5b00006> for mediated electrochemical determination of electron-accepting and electron-donating capacity, Kluepfel, Piepenbrock, Kappler and Sander (2014) <doi:10.1038/ngeo2084> for regeneration of electron-accepting capacity across repeated anoxic periods, Thompson, Chadwick, Rancourt and Chorover (2006) <doi:10.1016/j.gca.2005.12.005> for the increase in iron-oxide crystallinity under redox oscillation, and Keiluweit, Wanzek, Kleber, Nico and Fendorf (2017) <doi:10.1038/s41467-017-01406-6> for anaerobic microsites in otherwise aerobic soil. Agreement statistics follow Lin (1989) <doi:10.2307/2532051> and Kobayashi and Salam (2000) <doi:10.2134/agronj2000.922345x>.

We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<DOI:10.1111/biom.13024> for more information.

Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced.

This package provides a unified framework for finite-mixture latent variable models, including latent class analysis (LCA), latent profile analysis (LPA), latent class/profile analysis with covariates, and latent transition analysis (LTA), within one consistent interface. Estimation methods include the expectation-maximization (EM) algorithm; neural network estimation, which requires Python and its dependent libraries; integration with Mplus', which requires an installed copy of Mplus'; and stochastic EM (SEM) through the optional flexmix', Rmixmod', and RMixtComp backends. flexmix and the default Rmixmod path perform configurable warm-up trajectories and promote the best candidates to full SEM replications. Rmixmod additionally exposes its native strategy interface, including chained SEM-to-EM estimation, whereas RMixtComp exposes its native SEM and Gibbs controls without the external warm-up stage. Model assessment includes the Akaike information criterion (AIC), Bayesian information criterion (BIC), Schwarz information criterion (SIC), consistent AIC (CAIC), approximate weight of evidence (AWE), sample-size-adjusted BIC (SABIC), entropy, and average posterior probabilities. Model-comparison procedures include the ordinary likelihood-ratio test, the Mplus TECH11 Vuong-Lo-Mendell-Rubin and adjusted Lo-Mendell-Rubin tests, and fixed-replicate or sequential parametric bootstrap likelihood-ratio tests. Standard errors can be estimated by nonparametric bootstrap, numerical observed information, or analytic observed information based on Louis identity. Classification-error-adjusted maximum-likelihood and Bolck-Croon-Hagenaars three-step methods support covariates predicting latent membership, initial-status and transition regressions, and latent classes or states predicting continuous and categorical external observed dependent variables. Simulation, posterior-probability, classification-error, extraction, summary, plotting, model-adjustment, and update utilities are also provided for reproducible workflows.

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. Changes from the previous 1.5.3 release on CRAN are available at <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.

Implementation of Testlet and Item Response Theory. A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional and multidimensional Item Response Theory (IRT; Baker & Kim (2004) <doi:10.1201/9781482276725>) and Testlet Response Theory (TRT; Wainer et al., (2007) <doi:10.1017/CBO9780511618765>). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration. Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint Maximum Likelihood, the package also offers Bayesian estimation using priors with maximum a posteriori (MAP) method for unidimensional item response theory models. It also provides functions for scale linking and equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability across mixed-format test forms. It also facilitates fixed-parameter calibration, enabling users to estimate person abilities with known item parameters or vice versa, which is essential for pre-equating studies and item bank maintenance. Comprehensive data simulation functions are included to generate synthetic datasets with complex structures, including mixed-model blocks and specific testlet effects, aiding in methodological research and study design validation. Researchers can try multiple simulation situations. A suite of post-estimation tools is also provided, including item and test information functions with the conditional standard error of measurement, summed-score to scale-score conversion tables (expected a posteriori, weighted likelihood, and maximum likelihood), person-fit and item-fit statistics, local dependence diagnostics (Yen's Q3), differential item functioning (M-H and logistic regression), reliability coefficients, test characteristic curves, and mixture (latent-class) item response models.

In many cases, experiments must be repeated across multiple seasons or locations to ensure applicability of findings. A single experiment conducted in one location and season may yield limited conclusions, as results can vary under different environmental conditions. In agricultural research, treatment à location and treatment à season interactions play a crucial role. Analyzing a series of experiments across diverse conditions allows for more generalized and reliable recommendations. The CANE package facilitates the pooled analysis of experiments conducted over multiple years, seasons, or locations. It is designed to assess treatment interactions with environmental factors (such as location and season) using various experimental designs. The package supports pooled analysis of variance (ANOVA) for the following designs: (1) PooledCRD()': completely randomized design; (2) PooledRBD()': randomized block design; (3) PooledLSD()': Latin square design; (4) PooledSPD()': split plot design; and (5) PooledStPD()': strip plot design. Each function provides the following outputs: (i) Individual ANOVA tables based on independent analysis for each location or year; (ii) Testing of homogeneity of error variances among distinct locations using Bartlettâ s Chi-Square test; (iii) If Bartlettâ s test is significant, Aitkenâ s transformation, defined as the ratio of the response to the square root of the error mean square, is applied to the response variable; otherwise, the data is used as is; (iv) Combined analysis to obtain a pooled ANOVA table; (v) Multiple comparison tests, including Tukey's honestly significant difference (Tukey's HSD) test, Duncanâ s multiple range test (DMRT), and the least significant difference (LSD) test, for treatment comparisons. The statistical theory and steps of analysis of these designs are available in Dean et al. (2017)<doi:10.1007/978-3-319-52250-0> and Ruà z et al. (2024)<doi:10.1007/978-3-031-65575-3>. By broadening the scope of experimental conclusions, CANE enables researchers to derive robust, widely applicable recommendations. This package is particularly valuable in agricultural research, where accounting for treatment à location and treatment à season interactions is essential for ensuring the validity of findings across multiple settings.

Estimates heterogeneous coefficient models for large panels with cross-sectional dependence. Implements the Mean Group (MG) estimator of Pesaran and Smith (1995) <doi:10.1016/0304-4076(94)01644-F>, the Common Correlated Effects (CCE) and Dynamic CCE (DCCE) estimators of Pesaran (2006) <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran (2015) <doi:10.1016/j.jeconom.2015.03.007>, the regularized CCE of Juodis (2022), the Augmented Mean Group (AMG) of Eberhardt and Teal (2010), the Interactive Fixed Effects (IFE) estimator of Bai (2009) <doi:10.3982/ECTA6135>, and long-run estimators including Cross-Sectionally augmented Distributed Lag (CS-DL), Cross-Sectionally augmented Autoregressive Distributed Lag (CS-ARDL), and Pooled Mean Group (PMG) (Chudik et al. 2016; Shin et al. 1999). Also provides rolling-window estimation, high-dimensional fixed effect absorption, spatial CCE via user-supplied weight matrices, and structural break tests (Chow and sup-Wald) following Andrews (1993), Bai and Perron (1998), and Ditzen, Karavias and Westerlund (2024). Supplies a comprehensive cross-sectional dependence (CD) test suite including the Pesaran (2015) CD test <doi:10.1080/07474938.2014.956623>, the Juodis and Reese (2022) randomized weighted CD (CDw) test, the Baltagi et al. (2012) bias-adjusted weighted CD (CDw+) test, the Fan et al. (2015) Power Enhancement Approach (PEA) test, and the Pesaran and Xie (2021) bias-corrected CD (CD*) test. Further diagnostics include the Pesaran (2007) Cross-sectionally Augmented IPS (CIPS) panel unit root test <doi:10.1002/jae.951>, the Westerlund (2007) panel cointegration tests, the Dumitrescu and Hurlin (2012) panel Granger causality test, the Im-Pesaran-Shin (IPS) and Levin-Lin-Chu (LLC) panel unit root tests, the Pedroni (2004) and Kao (1999) residual cointegration tests, the Swamy (1970) and Pesaran and Yamagata (2008) slope homogeneity tests, a Hausman-type test for MG versus pooled, the exponent of cross-sectional dependence from Bailey et al. (2016) <doi:10.1002/jae.2490>, information criteria for Cross-Sectional Average (CSA) selection, the rank condition classifier, impulse response functions, cross-section and wild bootstrap inference, and broom'-compatible methods.

Understanding the current status of forest resources is essential for monitoring changes in forest ecosystems and generating related statistics. In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform <https://kfss.forest.go.kr/stat/ptl/article/articleList.do?curMenu=11694&bbsId=microdataboard> to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function.

This package provides tools for estimating length-based indicators from length frequency data to assess fish stock status and manage fisheries sustainably. Implements methods from Cope and Punt (2009) <doi:10.1577/C08-025.1> for data-limited stock assessment and Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> for detecting overfishing using simple indicators. Key functions include: FrequencyTable(): Calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range. A numeric value specifying the bin width for class intervals. If not provided, the bin width is automatically calculated using Wang (2020) <doi:10.1016/j.fishres.2019.105474> formula. FreqTM(): Creates a frequency distribution table for fish length data across multiple months using a consistent length class structure. The bin width is determined by either a custom value or Wang's formula, applied uniformly across all months. The function dynamically detects and renames columns to Month and Length from the input dataframe. The maximum observed length is included as part of the last class, with the upper bound set to the smallest multiple of the bin width greater than or equal to the maximum length. Months can be converted to dates using a configurable day and year, with dates assigned sequentially in day.month.year format (e.g., 15.01.26). FishPar(): Calculates length-based indicators (LBIs) proposed by Froese (2004) <doi:10.1111/j.1467-2979.2004.00144.x> such as the percentage of mature fish (Pmat), percentage of optimal length fish (Popt), percentage of mega spawners (Pmega), and the sum of these as Pobj. This function also estimates confidence intervals for different lengths, visualizes length frequency distributions, and provides data frames containing calculated values. FishSS(): Makes decisions based on input from Cope and Punt (2009) <doi:10.1577/C08-025.1> and parameters calculated by FishPar() (e.g., Pobj, Pmat, Popt, LM_ratio) to determine stock status as target spawning biomass (TSB40) and limit spawning biomass (LSB25), and selectivity. LWR(): Fits and visualizes length-weight relationships using linear regression, with options for log-transformation and customizable plotting.

This package provides methods for design, measurement, and analysis, with the aim of being user friendly yet methodologically sound. DMAR (pronounced "Dee-Mar") implements many advanced and nonstandard methods and makes them available for straightforward use, with interfaces, defaults, and documentation that are consistent across the package and grounded in the methodological literature, in support of sound and reproducible results. The package emphasizes effect size estimation with confidence intervals; sample size planning through accuracy in parameter estimation (AIPE) and power analysis (including composite power for designs whose conclusions require several results to hold at once), with minimum risk, sequential, and equivalence frameworks; reliability, agreement, and measurement more broadly, from coefficient omega with confidence intervals to measurement invariance; factor analysis and structural equation modeling, in which constructs, latent variables measured by multiple indicators, are modeled directly, with confirmatory factor analysis, convergent and discriminant validity, and sample size planning for structural equation models; mediation analysis, from the simple mediation model with bootstrap intervals to likelihood ratio tests of arbitrary indirect effects by model-based constrained optimization (MBCO), with multiple groups and the probing of moderated mediation; equivalence and noninferiority testing; meta-analysis; repeated measures, multivariate, ANOVA, and ANCOVA designs; and inference grounded in model comparison throughout. Measurement is approached from a psychometric perspective, and although many of the methods grew up in human-centered research, they apply broadly across the empirical sciences. Much of what is implemented traces to the author's methodological work, interests, and collaborations. DMAR is a more modern, more general, and greatly expanded reimagining of the MBESS package (Kelley, 2007a, <doi:10.18637/jss.v020.i08>; 2007b, <doi:10.3758/BF03192993>), which has been on CRAN for more than two decades and remains available there in stable form. Most functions accept either raw data or the summary statistics typically reported in published articles, so an analysis can be reproduced from a paper without the original data, which is useful both for extending a published analysis and for meta-analytic work. The estimation, inference, and planning functions return one consistently formatted data frame per function that composes with the broader R ecosystem, and confidence intervals are reported alongside effect sizes throughout, as best practice recommends. Researchers who have data and a question but who are not R experts will find the package approachable, while methodologists gain access to advanced and nonstandard methods, including tables of critical values not available elsewhere.