Helps calculate statistical values commonly used in meta-analysis. It provides several methods to compute different forms of standardized mean differences, as well as other values such as standard errors and standard deviations. The methods used in this package are described in the following references: Altman D G, Bland J M. (2011) <doi:10.1136/bmj.d2090> Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) <doi:10.1002/9780470743386.ch4> Chinn S. (2000) <doi:10.1002/1097-0258(20001130)19:22%3C3127::aid-sim784%3E3.0.co;2-m> Cochrane Handbook (2011) <https://handbook-5-1.cochrane.org/front_page.htm> Cooper, H., Hedges, L. V., & Valentine, J. C. (2009) <https://psycnet.apa.org/record/2009-05060-000> Cohen, J. (1977) <https://psycnet.apa.org/record/1987-98267-000> Ellis, P.D. (2009) <https://www.psychometrica.de/effect_size.html> Goulet-Pelletier, J.-C., & Cousineau, D. (2018) <doi:10.20982/tqmp.14.4.p242> Hedges, L. V. (1981) <doi:10.2307/1164588> Hedges L. V., Olkin I. (1985) <doi:10.1016/C2009-0-03396-0> Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et al. (2023) <doi:10.1136/bmj-2022-073141> Mayer M (2023) <https://search.r-project.org/CRAN/refmans/confintr/html/ci_proportion.html> Stackoverflow (2014) <https://stats.stackexchange.com/questions/82720/confidence-interval-around-binomial-estimate-of-0-or-1> Stackoverflow (2018) <https://stats.stackexchange.com/q/338043>.

Interface to the Raster API of the Geospatial Data Abstraction Library ('GDAL', <https://gdal.org>). Bindings are implemented in an exposed C++ class encapsulating a GDALDataset and its raster band objects, along with several stand-alone functions. These support manual creation of uninitialized datasets, creation from existing raster as template, read/set dataset parameters, low level I/O, color tables, raster attribute tables, virtual raster (VRT), and gdalwarp wrapper for reprojection and mosaicing. Includes GDAL algorithms ('dem_proc()', polygonize()', rasterize()', etc.), and functions for coordinate transformation and spatial reference systems. Calling signatures resemble the native C, C++ and Python APIs provided by the GDAL project. Includes raster calc() to evaluate a given R expression on a layer or stack of layers, with pixel x/y available as variables in the expression; and raster combine() to identify and count unique pixel combinations across multiple input layers, with optional output of the pixel-level combination IDs. Provides raster display using base graphics'. Bindings to a subset of the OGR API are also included for managing vector data sources. Bindings to a subset of the Virtual Systems Interface ('VSI') are also included to support operations on GDAL virtual file systems. These are general utility functions that abstract file system operations on URLs, cloud storage services, Zip'/'GZip'/'7z'/'RAR archives, and in-memory files. gdalraster may be useful in applications that need scalable, low-level I/O, or prefer a direct GDAL API.

This package implements the Oaxaca-Blinder decomposition method and generalizations of it that decompose differences in distributional statistics beyond the mean. The function ob_decompose() decomposes differences in the mean outcome between two groups into one part explained by different covariates (composition effect) and into another part due to differences in the way covariates are linked to the outcome variable (structure effect). The function further divides the two effects into the contribution of each covariate and allows for weighted doubly robust decompositions. For distributional statistics beyond the mean, the function performs the recentered influence function (RIF) decomposition proposed by Firpo, Fortin, and Lemieux (2018). The function dfl_decompose() divides differences in distributional statistics into an composition effect and a structure effect using inverse probability weighting as introduced by DiNardo, Fortin, and Lemieux (1996). The function also allows to sequentially decompose the composition effect into the contribution of single covariates. References: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2018) <doi:10.3390/econometrics6020028>. "Decomposing Wage Distributions Using Recentered Influence Function Regressions." Fortin, Nicole M., Thomas Lemieux, and Sergio Firpo. (2011) <doi:10.3386/w16045>. "Decomposition Methods in Economics." DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. (1996) <doi:10.2307/2171954>. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach." Oaxaca, Ronald. (1973) <doi:10.2307/2525981>. "Male-Female Wage Differentials in Urban Labor Markets." Blinder, Alan S. (1973) <doi:10.2307/144855>. "Wage Discrimination: Reduced Form and Structural Estimates.".

Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) <arXiv:2103.06347>. For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint.

This package implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramà rez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramà rez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolà n-Dà az and Rubio-Ramà rez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The bsvarSIGNs package is aligned regarding objects, workflows, and code structure with the R package bsvars by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.

It generates summary statistics on the input dataset using different descriptive univariate statistical measures on entire data or at a group level. Though there are other packages which does similar job but each of these are deficient in one form or other, in the measures generated, in treating numeric, character and date variables alike, no functionality to view these measures on a group level or the way the output is represented. Given the foremost role of the descriptive statistics in any of the exploratory data analysis or solution development, there is a need for a more constructive, structured and refined version over these packages. This is the idea behind the package and it brings together all the required descriptive measures to give an initial understanding of the data quality, distribution in a faster,easier and elaborative way.The function brings an additional capability to be able to generate these statistical measures on the entire dataset or at a group level. It calculates measures of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures are generated based on the data types as required by them,rather than applying numerical measures on character and data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a summary report for the data.

Transfer learning, aiming to use auxiliary domains to help improve learning of the target domain of interest when multiple heterogeneous datasets are available, has always been a hot topic in statistical machine learning. The recent transfer learning methods with statistical guarantees mainly focus on the overall parameter transfer for supervised models in the ideal case with the informative auxiliary domains with overall similarity. In contrast, transfer learning for unsupervised graph learning is in its infancy and largely follows the idea of overall parameter transfer as for supervised learning. In this package, the transfer learning for several complex graphical models is implemented, including Tensor Gaussian graphical models, non-Gaussian directed acyclic graph (DAG), and Gaussian graphical mixture models. Notably, this package promotes local transfer at node-level and subgroup-level in DAG structural learning and Gaussian graphical mixture models, respectively, which are more flexible and robust than the existing overall parameter transfer. As by-products, transfer learning for undirected graphical model (precision matrix) via D-trace loss, transfer learning for mean vector estimation, and single non-Gaussian learning via topological layer method are also included in this package. Moreover, the aggregation of auxiliary information is an important issue in transfer learning, and this package provides multiple user-friendly aggregation methods, including sample weighting, similarity weighting, and most informative selection. Reference: Ren, M., Zhen Y., and Wang J. (2022) <arXiv:2211.09391> "Transfer learning for tensor graphical models". Ren, M., He X., and Wang J. (2023) <arXiv:2310.10239> "Structural transfer learning of non-Gaussian DAG". Zhao, R., He X., and Wang J. (2022) <https://jmlr.org/papers/v23/21-1173.html> "Learning linear non-Gaussian directed acyclic graph with diverging number of nodes".

Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study.

The t-designs represent a generalized class of balanced incomplete block designs in which the number of blocks in which any t-tuple of treatments (t >= 2) occur together is a constant. When the focus of an experiment lies in grading and selecting treatment subgroups, t-designs would be preferred over the conventional ones, as they have the additional advantage of t-tuple balance. t-designs can be advantageously used in identifying the best crop-livestock combination for a particular location in Integrated Farming Systems that will help in generating maximum profit. But as the number of components increases, the number of possible t-component combinations will also increase. Most often, combinations derived from specific components are only practically feasible, for example, in a specific locality, farmers may not be interested in keeping a pig or goat and hence combinations involving these may not be of any use in that locality. In such situations partially balanced t-designs with few selected combinations appearing in a constant number of blocks (while others not at all appearing) may be useful (Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2021)<doi:10.1080/03610918.2021.2008436>). Further, every location may not have the resources to form equally sized homogeneous blocks. Partially balanced t-designs with unequal block sizes (Damaraju Raghavarao & Bei Zhou (1998)<doi:10.1080/03610929808832657>. Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2022)." Partially Balanced t-designs with unequal block sizes") prove to be more suitable for such situations.This package generates three series of partially balanced t-designs namely Series 1, Series 2 and Series 3. Series 1 and Series 2 are designs having equal block sizes and with treatment structures 4(t + 1) and a prime number, respectively. Series 3 consists of designs with unequal block sizes and with treatment structure n(n-1)/2. This package is based on the function named PBtD() for generating partially balanced t-designs along with their parameters, information matrices, average variance factors and canonical efficiency factors.

Advances in automated document classification has led to identifying massive numbers of clinical concepts from handwritten clinical notes. These high dimensional clinical concepts can serve as highly informative predictors in building classification algorithms for identifying patients with different clinical conditions, commonly referred to as patient phenotyping. However, from a planning perspective, it is critical to ensure that enough data is available for the phenotyping algorithm to obtain a desired classification performance. This challenge in sample size planning is further exacerbated by the high dimension of the feature space and the inherent imbalance of the response class. Currently available sample size planning methods can be categorized into: (i) model-based approaches that predict the sample size required for achieving a desired accuracy using a linear machine learning classifier and (ii) learning curve-based approaches (Figueroa et al. (2012) <doi:10.1186/1472-6947-12-8>) that fit an inverse power law curve to pilot data to extrapolate performance. We develop model-based approaches for imbalanced data with correlated features, deriving sample size formulas for performance metrics that are sensitive to class imbalance such as Area Under the receiver operating characteristic Curve (AUC) and Matthews Correlation Coefficient (MCC). This is done using a two-step approach where we first perform feature selection using the innovated High Criticism thresholding method (Hall and Jin (2010) <doi:10.1214/09-AOS764>), then determine the sample size by optimizing the two performance metrics. Further, we develop software in the form of an R package named planningML and an R Shiny app to facilitate the convenient implementation of the developed model-based approaches and learning curve approaches for imbalanced data. We apply our methods to the problem of phenotyping rare outcomes using the MIMIC-III electronic health record database. We show that our developed methods which relate training data size and performance on AUC and MCC, can predict the true or observed performance from linear ML classifiers such as LASSO and SVM at different training data sizes. Therefore, in high-dimensional classification analysis with imbalanced data and correlated features, our approach can efficiently and accurately determine the sample size needed for machine-learning based classification.

Test procedures and break point estimators for persistent processes that exhibit structural breaks in mean or in persistence. On the one hand the package contains the most popular approaches for testing whether a time series exhibits a break in persistence from I(0) to I(1) or vice versa, such as those of Busetti and Taylor (2004) and Leybourne, Kim, and Taylor (2007). The approach by Martins and Rodrigues (2014), which allows to detect changes from I(d1) to I(d2) with d1 and d2 being non-integers, is included as well. In case the tests reject the null of constant persistence, various breakpoint estimators are available to detect the point of the break as well as the order of integration in the two regimes. On the other hand the package contains the most popular approaches to test for a change-in-mean of a long-memory time series, which were recently reviewed by Wenger, Leschinski, and Sibbertsen (2018). These include memory robust versions of the CUSUM, sup-Wald, and Wilcoxon type tests. The tests either utilize consistent estimates of the long-run variance or a self normalization approach in their test statistics. Betken (2016) <doi:10.1111/jtsa.12187> Busetti and Taylor (2004) <doi:10.1016/j.jeconom.2003.10.028> Dehling, Rooch and Taqqu (2012) <doi:10.1111/j.1467-9469.2012.00799.x> Harvey, Leybourne and Taylor (2006) <doi:10.1016/j.jeconom.2005.07.002> Horvath and Kokoszka (1997) <doi:10.1016/S0378-3758(96)00208-X> Hualde and Iacone (2017) <doi:10.1016/j.econlet.2016.10.014> Iacone, Leybourne and Taylor (2014) <doi:10.1111/jtsa.12049> Leybourne, Kim, Smith, and Newbold (2003) <doi:10.1111/1368-423X.t01-1-00110> Leybourne and Taylor (2004) <doi:10.1016/j.econlet.2003.12.015> Leybourne, Kim, and Taylor (2007): <doi:10.1111/j.1467-9892.2006.00517.x> Martins and Rodrigues (2014) <doi:10.1016/j.csda.2012.07.021> Shao (2011) <doi:10.1111/j.1467-9892.2010.00717.x> Sibbertsen and Kruse (2009) <doi:10.1111/j.1467-9892.2009.00611.x> Wang (2008) <doi:10.1080/00949650701216604> Wenger, Leschinski and Sibbertsen (2018) <doi:10.1016/j.econlet.2017.12.007>.

Generate continuous (normal, non-normal, or mixture distributions), binary, ordinal, and count (regular or zero-inflated, Poisson or Negative Binomial) variables with a specified correlation matrix, or one continuous variable with a mixture distribution. This package can be used to simulate data sets that mimic real-world clinical or genetic data sets (i.e., plasmodes, as in Vaughan et al., 2009 <DOI:10.1016/j.csda.2008.02.032>). The methods extend those found in the SimMultiCorrData R package. Standard normal variables with an imposed intermediate correlation matrix are transformed to generate the desired distributions. Continuous variables are simulated using either Fleishman (1978)'s third order <DOI:10.1007/BF02293811> or Headrick (2002)'s fifth order <DOI:10.1016/S0167-9473(02)00072-5> polynomial transformation method (the power method transformation, PMT). Non-mixture distributions require the user to specify mean, variance, skewness, standardized kurtosis, and standardized fifth and sixth cumulants. Mixture distributions require these inputs for the component distributions plus the mixing probabilities. Simulation occurs at the component level for continuous mixture distributions. The target correlation matrix is specified in terms of correlations with components of continuous mixture variables. These components are transformed into the desired mixture variables using random multinomial variables based on the mixing probabilities. However, the package provides functions to approximate expected correlations with continuous mixture variables given target correlations with the components. Binary and ordinal variables are simulated using a modification of ordsample() in package GenOrd'. Count variables are simulated using the inverse CDF method. There are two simulation pathways which calculate intermediate correlations involving count variables differently. Correlation Method 1 adapts Yahav and Shmueli's 2012 method <DOI:10.1002/asmb.901> and performs best with large count variable means and positive correlations or small means and negative correlations. Correlation Method 2 adapts Barbiero and Ferrari's 2015 modification of the GenOrd package <DOI:10.1002/asmb.2072> and performs best under the opposite scenarios. The optional error loop may be used to improve the accuracy of the final correlation matrix. The package also contains functions to calculate the standardized cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables.

This package provides the users with the ability to quickly create linked micromap plots for a collection of geographic areas. Linked micromap plots are visualizations of geo-referenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the micromapST function. Contained in this package are border group datasets to support creating linked micromap plots for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create linked micromap plots for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package with the BuildBorderGroup function. In April of 2022, it was announced that maptools', rgdal', and rgeos R packages would be retired in middle to end of 2023 and removed from the CRAN libraries. The BuildBorderGroup function was dependent on these packages. micromapST functions were not impacted by the retired R packages. Upgrading of BuildBorderGroup function was completed and released with version 3.0.0 on August 10, 2023 using the sf R package. References: Carr and Pickle, Chapman and Hall/CRC, Visualizing Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson, and Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., <https://www.jstatsoft.org/v63/i03/>. Copyrighted 2013, 2014, 2015, 2016, 2022, 2023, 2024, and 2025 by Carr, Pearson and Pickle.

The Greymodels Shiny app is an interactive interface for statistical modelling and forecasting using grey-based models. It covers several state-of-the-art univariate and multivariate grey models. A user friendly interface allows users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within user chosen confidence intervals. Chang, C. (2019) <doi:10.24818/18423264/53.1.19.11>, Li, K., Zhang, T. (2019) <doi:10.1007/s12667-019-00344-0>, Ou, S. (2012) <doi:10.1016/j.compag.2012.03.007>, Li, S., Zhou, M., Meng, W., Zhou, W. (2019) <doi:10.1080/23307706.2019.1666310>, Xie, N., Liu, S. (2009) <doi:10.1016/j.apm.2008.01.011>, Shao, Y., Su, H. (2012) <doi:10.1016/j.aasri.2012.06.003>, Xie, N., Liu, S., Yang, Y., Yuan, C. (2013) <doi:10.1016/j.apm.2012.10.037>, Li, S., Miao, Y., Li, G., Ikram, M. (2020) <doi:10.1016/j.matcom.2019.12.020>, Che, X., Luo, Y., He, Z. (2013) <doi:10.4028/www.scientific.net/AMM.364.207>, Zhu, J., Xu, Y., Leng, H., Tang, H., Gong, H., Zhang, Z. (2016) <doi:10.1109/appeec.2016.7779929>, Luo, Y., Liao, D. (2012) <doi:10.4028/www.scientific.net/AMR.507.265>, Bilgil, H. (2020) <doi:10.3934/math.2021091>, Li, D., Chang, C., Chen, W., Chen, C. (2011) <doi:10.1016/j.apm.2011.04.006>, Chen, C. (2008) <doi:10.1016/j.chaos.2006.08.024>, Zhou, W., Pei, L. (2020) <doi:10.1007/s00500-019-04248-0>, Xiao, X., Duan, H. (2020) <doi:10.1016/j.engappai.2019.103350>, Xu, N., Dang, Y. (2015) <doi:10.1155/2015/606707>, Chen, P., Yu, H.(2014) <doi:10.1155/2014/242809>, Zeng, B., Li, S., Meng, W., Zhang, D. (2019) <doi:10.1371/journal.pone.0221333>, Liu, L., Wu, L. (2021) <doi:10.1016/j.apm.2020.08.080>, Hu, Y. (2020) <doi:10.1007/s00500-020-04765-3>, Zhou, P., Ang, B., Poh, K. (2006) <doi:10.1016/j.energy.2005.12.002>, Cheng, M., Li, J., Liu, Y., Liu, B. (2020) <doi:10.3390/su12020698>, Wang, H., Wang, P., Senel, M., Li, T. (2019) <doi:10.1155/2019/9049815>, Ding, S., Li, R. (2020) <doi:10.1155/2020/4564653>, Zeng, B., Li, C. (2018) <doi:10.1016/j.cie.2018.02.042>, Xie, N., Liu, S. (2015) <doi:10.1109/JSEE.2015.00013>, Zeng, X., Yan, S., He, F., Shi, Y. (2019) <doi:10.1016/j.apm.2019.11.032>.

Data from Gardner and Janson art history textbooks about both the artists featured in these books as well as their works. See Helen Gardner ("Art through the ages; an introduction to its history and significance," 1926, <https://find.library.duke.edu/catalog/DUKE000104481>. Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1980, ISBN: 0155037587). Fred S. Kleiner ("Gardnerâ s art through the ages: a global history," 2020, ISBN: 9781337630702). Horst de la Croix and Richard G. Tansey ("Gardner's art through the ages," 1986, ISBN: 0155037633). Helen Gardner ("Art through the ages; an introduction to its history and significance," 1936, <https://find.library.duke.edu/catalog/DUKE001199463>). Helen Gardner ("Art through the ages," 1948, <https://find.library.duke.edu/catalog/DUKE001199466>). Helen Gardner, revised under the editorship of Sumner M. Crosby ("Art through the ages," 1959, <https://find.library.duke.edu/catalog/DUKE001199469>). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1975, ISBN: 0155037560). Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2013, ISBN: 9780495915423. Fred S. Kleiner, Christin J. Mamiya, Richard G. Tansey ("Gardnerâ s art through the ages," 2001, ISBN: 0155083155). Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2016, ISBN: 9781285837840). Fred S. Kleiner, Christin J. Mamiya ("Gardnerâ s art through the ages," 2005, ISBN: 0534640958). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1970, ISBN: 0155037528). Helen Gardner, Richard G. Tansey, Fred S. Kleiner ("Gardnerâ s Art through the ages," 1996, ISBN: 0155011413). Helen Gardner, Horst de la Croix, Richard G. Tansey, Diane Kirkpatrick ("Gardnerâ s Art through the ages," 1991, ISBN: 0155037692). Helen Gardner, Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2009, ISBN: 9780495093077). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Jansonâ s history of art: the western tradition," 2007, ISBN: 0131934554). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Jansonâ s history of art: the western tradition," 2011, ISBN: 9780205685172). H. W. Janson, Anthony F. Janson ("History of Art," 2001, ISBN: 0810934469). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1986, ISBN: 013389388). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1977, ISBN: 0810910527). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1969, <https://find.library.duke.edu/catalog/DUKE000005734>). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1963, <https://find.library.duke.edu/catalog/DUKE001521852>). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1991, ISBN: 0810934019). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1995, ISBN: 0810934213).

AllCurves() runs multiple lactation curve models and extracts selection criteria for each model. This package summarises the most common lactation curve models from the last century and provides a tool for researchers to quickly decide on which model fits their data best to proceed with their analysis. Start parameters were optimized based on a dataset with 1.7 million Holstein-Friesian cows. If convergence fails, the start parameters need to be manually adjusted. The models included in the package are taken from: (1) Michaelis-Menten: Michaelis, L. and M.L. Menten (1913). <www.plantphys.info/plant_physiology/copyright/MichaelisMentenTranslation2.pdf> (1a) Michaelis-Menten (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). <doi:10.1017/S002185960007684X> (1b) Michaelis-Menten + exponential (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). <doi:10.1017/S002185960007684X> (2) Brody (1923): Brody, S., A.C. Ragsdale, and C.W. Turner (1923). <doi:10.1085/jgp.5.6.777> (3) Brody (1924): Brody, S., C.W. Tuner, and A.C. Ragsdale (1924). <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2140670/> (4) Schumacher: Schumacher, F.X. (1939) in Thornley, J.H.M. and J. France (2007). <https://books.google.com.au/books/about/Mathematical_Models_in_Agriculture.html?id=rlwBCRSHobcC&redir_esc=y> (4a) Schumacher (Lopez et al. 2015): Lopez, S. J. France, N.E. Odongo, R.A. McBride, E. Kebreab, O. AlZahal, B.W. McBride, and J. Dijkstra (2015). <doi:10.3168/jds.2014-8132> (5) Parabolic exponential (Adediran): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (6) Wood: Wood, P.D.P. (1967). <doi:10.1038/216164a0> (6a) Wood reparameterized (Dhanoa): Dhanoa, M.S. (1981). <doi:10.1017/S0003356100027276> (6b) Wood non-linear (Cappio-Borlino): Cappio-Borlino, A., G. Pulina, and G. Rossi (1995). <doi:10.1016/0921-4488(95)00713-U> (7) Quadratic Polynomial (Dave): Dave, B.K. (1971) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (8) Cobby and Le Du (Vargas): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). <doi:10.3168/jds.S0022-0302(00)75005-3> (9) Papajcsik and Bodero 1: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (10) Papajcsik and Bodero 2: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (11) Papajcsik and Bodero 3: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (12) Papajcsik and Bodero 4: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (13) Papajcsik and Bodero 6: Papajcsik, I.A. and J. Bodero (1988). <doi:10.1017/S0003356100003275> (14) Mixed log model 1 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (15) Mixed log model 3 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (16) Log-quadratic (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (17) Wilmink: J.B.M. Wilmink (1987). <doi:10.1016/0301-6226(87)90003-0> (17a) modified Wilmink (Jakobsen): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (17b) modified Wilmink (Laurenson & Strucken): Strucken E.M., Brockmann G.A., and Y.C.S.M. Laurenson (2019). <http://www.aaabg.org/aaabghome/AAABG23papers/35Strucken23139.pdf> (18) Bicompartemental (Ferguson and Boston 1993): Ferguson, J.D., and R. Boston (1993) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (19) Dijkstra: Dijkstra, J., J. France, M.S. Dhanoa, J.A. Maas, M.D. Hanigan, A.J. Rook, and D.E. Beever (1997). <doi:10.3168/jds.S0022-0302(97)76185-X> (20) Morant and Gnanasakthy (Pollott et al 2000): Pollott, G.E. and E. Gootwine (2000). <doi:10.1017/S1357729800055028> (21) Morant and Gnanasakthy (Vargas et al 2000): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). <doi:10.3168/jds.S0022-0302(00)75005-3> (22) Morant and Gnanasakthy (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (23) Khandekar (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). <https://journal.interbull.org/index.php/ib/issue/view/11> (24) Ali and Schaeffer: Ali, T.E. and L.R. Schaeffer (1987). <https://cdnsciencepub.com/doi/pdf/10.4141/cjas87-067> (25) Fractional Polynomial (Elvira et al. 2013): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). <doi:10.1017/S175173111200239X> (26) Pollott multiplicative (Elvira): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). <doi:10.1017/S175173111200239X> (27) Pollott modified: Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). <doi:10.3168/jds.2011-4663> (28) Monophasic Grossman: Grossman, M. and W.J. Koops (1988). <doi:10.3168/jds.S0022-0302(88)79723-4> (29) Monophasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (30) Diphasic (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (31) Diphasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). <doi:10.3168/jds.S0022-0302(99)75464-0> (32) Legendre Polynomial (3th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (33) Legendre Polynomial (4th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). <doi:10.3168/jds.S0022-0302(02)74231-8> (34) Legendre + Wilmink (Lidauer): Lidauer, M. and E.A. Mantysaari (1999). <https://journal.interbull.org/index.php/ib/article/view/417> (35) Natural Cubic Spline (3 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). <doi:10.3168/jds.S0022-0302(99)75277-X> (36) Natural Cubic Spline (4 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). <doi:10.3168/jds.S0022-0302(99)75277-X> (37) Natural Cubic Spline (5 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999) <doi:10.3168/jds.S0022-0302(99)75277-X> (38) Natural Cubic Spline (defined knots according to Harrell 2001): Jr. Harrell, F.E. (2001). <https://link.springer.com/book/10.1007/978-3-319-19425-7> The selection criteria measure the goodness of fit of the model and include: Residual standard error (RSE), R-square (R2), log likelihood, Akaike information criterion (AIC), Akaike information criterion corrected (AICC), Bayesian Information Criterion (BIC), Durbin Watson coefficient (DW). The following model parameters are included: Residual sum of squares (RSS), Residual standard deviation (RSD), F-value (F) based on F-ratio test.

Automatically generated RnBeads annotation package for the assembly rn5.

RNA-seq, sample size.

Pure Rust bzip2 decompressor.

This package provides Actix runtime.

This package provides Actix runtime.

This package provides Actix runtime.

Sundry discrete probability distributions and helper functions.

This crate provides a wrapper for SQLite.