Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application. A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information <https://github.com/hakkisabah/tsentiment> ). In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information <https://github.com/hakkisabah/tsentiment-services> ). After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule. tsentiment package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at <https://developer.twitter.com/en/docs/twitter-api/v1/rate-limits>.

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>.

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".

API bindings to the Geospatial Data Abstraction Library ('GDAL', <https://gdal.org>). Implements the GDAL Raster and Vector Data Models. Bindings are implemented with Rcpp modules. Exposed C++ classes and stand-alone functions wrap much of the GDAL API and provide additional functionality. Calling signatures resemble the native C, C++ and Python APIs provided by the GDAL project. Class GDALRaster encapsulates a GDALDataset and its raster band objects. Class GDALVector encapsulates an OGRLayer and the GDALDataset that contains it. Class VSIFile provides bindings to the GDAL VSIVirtualHandle API. Additional classes include CmbTable for counting unique combinations of integers, and RunningStats for computing summary statistics efficiently on large data streams. C++ stand-alone functions provide bindings to most GDAL raster and vector utilities including OGR facilities for vector geoprocessing, several algorithms, the Geometry API ('GEOS via GDAL headers), the Spatial Reference Systems API, and methods for coordinate transformation. Bindings to the Virtual Systems Interface ('VSI') API implement standard file system operations, abstracted for URLs, cloud storage services, Zip'/'GZip'/'7z'/'RAR', in-memory files, as well as regular local file systems. This provides a single interface for operating on file system objects that works the same for any storage backend. A custom raster calculator evaluates a user-defined R expression on a layer or stack of layers, with pixel x/y available as variables in the expression. Raster combine() identifies and counts unique pixel combinations across multiple input layers, with optional raster output of the pixel-level combination IDs. Basic plotting capability is provided for raster and vector display. gdalraster leans toward minimalism and the use of simple, lightweight objects for holding raw data. Currently, only minimal S3 class interfaces have been implemented for selected R objects that contain spatial data. gdalraster may be useful in applications that need scalable, low-level I/O, or prefer a direct GDAL API.

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.

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).

Pure Rust bzip2 decompressor.

This package provides Actix runtime.

This package provides Actix runtime.

This package provides Actix runtime.

This crate provides a wrapper for SQLite.

This crate provides a wrapper for SQLite.

This crate provides a wrapper for SQLite.

This crate provides a wrapper for SQLite.

This package provides Ergonomic wrapper for SQLite.

Sundry discrete probability distributions and helper functions.