Calibrates cause-specific mortality fractions (CSMF) estimates generated by computer-coded verbal autopsy (CCVA) algorithms from WHO-standardized verbal autopsy (VA) survey data. It leverages data from the multi-country Child Health and Mortality Prevention Surveillance (CHAMPS) project <https://champshealth.org/>, which determines gold standard causes of death via Minimally Invasive Tissue Sampling (MITS). By modeling the CHAMPS data using the misclassification matrix modeling framework proposed in Pramanik et al. (2025, <doi:10.1214/24-AOAS2006>), the package includes an inventory of 48 uncertainty-quantified misclassification matrices for three CCVA algorithms (EAVA, InSilicoVA, InterVA), two age groups (neonates aged 0-27 days and children aged 1-59 months), and eight "countries" (seven countries in CHAMPS -- Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, South Africa -- and an estimate for countries not in CHAMPS). Given a VA-only data for an age group, CCVA algorithm, and country, the package uses the corresponding uncertainty-quantified misclassification matrix estimates as an informative prior, and utilizes the modular VA-calibration to produce calibrated CSMF estimates. It also supports ensemble calibration when VA-only data are provided for multiple algorithms. More generally, the package can be applied to calibrate predictions from a discrete classifier (or ensemble of classifiers) utilizing user-provided fixed or uncertainty-quantified misclassification matrices. This work is supported by the Bill and Melinda Gates Foundation Grant INV-034842.

This package provides tools to calculate stability indices with parametric, non-parametric and probabilistic approaches. The basic data format requirement for toolStability is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. Function "table_stability" offers the summary table of all stability indices in this package. This R package toolStability is part of the main publication: Wang, Casadebaig and Chen (2023) <doi:10.1007/s00122-023-04264-7>. Analysis pipeline for main publication can be found on github: <https://github.com/Illustratien/Wang_2023_TAAG>. Sample dataset in this package is derived from another publication: Casadebaig P, Zheng B, Chapman S et al. (2016) <doi:10.1371/journal.pone.0146385>. For detailed documentation of dataset, please see on Zenodo <doi:10.5281/zenodo.4729636>. Indices used in this package are from: Döring TF, Reckling M (2018) <doi:10.1016/j.eja.2018.06.007>. Eberhart SA, Russell WA (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. Eskridge KM (1990) <doi:10.2135/cropsci1990.0011183X003000020025x>. Finlay KW, Wilkinson GN (1963) <doi:10.1071/AR9630742>. Hanson WD (1970) Genotypic stability. <doi:10.1007/BF00285245>. Lin CS, Binns MR (1988). Nassar R, Hühn M (1987). Pinthus MJ (1973) <doi:10.1007/BF00021563>. Römer T (1917). Shukla GK (1972). Wricke G (1962).

Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The ashapesampler package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.

Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering, and Kemeny-equivalent augmented unfolding. It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). The package depends on the ConsRank R package. Options for validate the tree-based method are both test-set procedure and V-fold cross validation. The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index). The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function sacofSym from the package smacof'. Essential references: D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) <doi:10.1080/00273171.2021.1899892>; D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) <doi:10.1007/s00357-020-09367-0>; D'Ambrosio, A., and Heiser, W.J. (2019) <doi:10.1007/s41237-018-0069-5>; D'Ambrosio, A., and Heiser W.J. (2016) <doi:10.1007/s11336-016-9505-1>; Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) <doi:10.1109/TFUZZ.2011.2179303>; Marden, J.J. <ISBN:0412995212>.

Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual â hairballâ that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be â explodedâ to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which â explodesâ the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl]<https://github.com/UTMB-DIVA-Lab/epl> that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) <https://pmc.ncbi.nlm.nih.gov/articles/PMC5543384/>). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks.

This package performs analyzes and estimates of environmental covariates and genetic parameters related to selection strategies and development of superior genotypes. It has two main functionalities, the first being about prediction models of covariates and environmental processes, while the second deals with the estimation of genetic parameters and selection strategies. Designed for researchers and professionals in genetics and environmental sciences, the package combines statistical methods for modeling and data analysis. This includes the plastochron estimate proposed by Porta et al. (2024) <doi:10.1590/1807-1929/agriambi.v28n10e278299>, Stress indices for genotype selection referenced by Ghazvini et al. (2024) <doi:10.1007/s10343-024-00981-1>, the Environmental Stress Index described by Tazzo et al. (2024) <https://revistas.ufg.br/vet/article/view/77035>, industrial quality indices of wheat genotypes (Szareski et al., 2019), <doi:10.4238/gmr18223>, Ear Indexes estimation (Rigotti et al., 2024), <doi:10.13083/reveng.v32i1.17394>, Selection index for protein and grain yield (de Pelegrin et al., 2017), <doi:10.4236/ajps.2017.813224>, Estimation of the ISGR - Genetic Selection Index for Resilience for environmental resilience (Bandeira et al., 2024) <https://www.cropj.com/Carvalho_18_12_2024_825_830.pdf>, estimation of Leaf Area Index (Meira et al., 2015) <https://www.fag.edu.br/upload/revista/cultivando_o_saber/55d1ef202e494.pdf>, Restriction of control variability (Carvalho et al., 2023) <doi:10.4025/actasciagron.v45i1.56156>, Risk of Disease Occurrence in Soybeans described by Engers et al. (2024) <doi:10.1007/s40858-024-00649-1> and estimation of genetic parameters for selection based on balanced experiments (Yadav et al., 2024) <doi:10.1155/2024/9946332>.

This package provides tools to teach students elemental statistics. The main topics covered are descriptive statistics, probability models (discrete and continuous variables) and statistical inference (confidence intervals and hypothesis tests). One of the main advantages of this package is that allows the user to read quite a variety of types of data files with one unique command. Moreover it includes shortcuts to simple but up-to-now not in R descriptive features such a complete frequency table or an histogram with the optimal number of intervals. Related to model distributions (both discrete and continuous), the package allows the student to easy plot the mass/density function, distribution function and quantile function just detailing as input arguments the known population parameters. The inference related tools are basically confidence interval and hypothesis testing. Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared).

This package provides a set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) <doi:10.18637/jss.v100.i21>.

This package provides functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 <doi:10.1111/fwb.12520>) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the algaebase online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the algaebase administrators. Note that none of the algaeClassify authors are affiliated with algaebase in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the Global Names Resolver service via its API (<https://resolver.globalnames.org/api>). In addition, currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of species from the ITIS database using wrapper functions for the ritis package. The algaeClassify package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). DISCLAIMER: This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.

There are 4 possible methods: "ExhaustiveSearch"; "ExhaustivePhi"; "ClusteringSearch"; and "ClusteringPhi". "ExhaustiveSearch"--> gives you the best phage cocktail from a phage-bacteria infection network. It checks different phage cocktail sizes from 1 to 7 and only stops before if it lyses all bacteria. Other option is when users have decided not to obtain a phage cocktail size higher than a limit value. "ExhaustivePhi"--> firstly, it finds Phi out. Phi is a formula indicating the necessary phage cocktail size. Phi needs nestedness temperature and fill, which are internally calculated. This function will only look for the best combination (phage cocktail) with a Phi size. "ClusteringSearch"--> firstly, an agglomerative hierarchical clustering using Ward's algorithm is calculated for phages. They will be clustered according to bacteria lysed by them. PhageCocktail() chooses how many clusters are needed in order to select 1 phage per cluster. Using the phages selected during the clustering, it checks different phage cocktail sizes from 1 to 7 and only stops before if it lyses all bacteria. Other option is when users have decided not to obtain a phage cocktail size higher than a limit value. "ClusteringPhi"--> firstly, an agglomerative hierarchical clustering using Ward's algorithm is calculated for phages. They will be clustered according to bacteria lysed by them. PhageCocktail() chooses how many clusters are needed in order to select 1 phage per cluster. Once the function has one phage per cluster, it calculates Phi. If the number of clusters is less than Phi number, it will be changed to obtain, as minimum, this quantity of candidates (phages). Then, it calculates the best combination of Phi phages using those selected during the clustering with Ward algorithm. If you use PhageCocktail, please cite it as: "PhageCocktail: An R Package to Design Phage Cocktails from Experimental Phage-Bacteria Infection Networks". Marà a Victoria Dà az-Galián, Miguel A. Vega-Rodrà guez, Felipe Molina. Computer Methods and Programs in Biomedicine, 221, 106865, Elsevier Ireland, Clare, Ireland, 2022, pp. 1-9, ISSN: 0169-2607. <doi:10.1016/j.cmpb.2022.106865>.

Automatically selects and visualises statistical hypothesis tests between two vectors, based on their class, distribution, sample size, and a user-defined confidence level (conf.level). Visual outputs - including box plots, bar charts, regression lines with confidence bands, mosaic plots, residual plots, and Q-Q plots - are annotated with relevant test statistics, assumption checks, and post-hoc analyses where applicable. The algorithmic workflow helps the user focus on the interpretation of test results rather than test selection. It is particularly suited for quick data analysis, e.g., in statistical consulting projects or educational settings. The test selection algorithm proceeds as follows: Input vectors of class numeric or integer are considered numerical; those of class factor are considered categorical. Assumptions of residual normality and homogeneity of variances are considered met if the corresponding test yields a p-value greater than the significance level alpha = 1 - conf.level. (1) When the response vector is numerical and the predictor vector is categorical, a test of central tendencies is selected. If the categorical predictor has exactly two levels, t.test() is applied when group sizes exceed 30 (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>). For smaller samples, normality of residuals is tested using shapiro.test(); if met, t.test() is used; otherwise, wilcox.test(). If the predictor is categorical with more than two levels, an aov() is initially fitted. Residual normality is evaluated using both shapiro.test() and ad.test(); residuals are considered approximately normal if at least one test yields a p-value above alpha. If this assumption is met, bartlett.test() assesses variance homogeneity. If variances are homogeneous, aov() is used; otherwise oneway.test(). Both tests are followed by TukeyHSD(). If residual normality cannot be assumed, kruskal.test() is followed by pairwise.wilcox.test(). (2) When both the response and predictor vectors are numerical, a simple linear regression model is fitted using lm(). (3) When both vectors are categorical, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is applied to test independence either by chisq.test() or fisher.test().

Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> b) Tsagris M. (2014). "The k-NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. <doi:10.6339/JDS.201407_12(3).0008>. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. <doi:10.48550/arXiv.1511.07600>. d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2): 47--57. <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>. e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554>. g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. <doi:10.1111/anzs.12289>. j) Alenazi A.A. (2022). "f-divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. <doi:10.18187/pjsor.v18i4.3969>. k) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. <doi:10.1007/978-3-031-08329-7_10>. l) Alenazi A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>. m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non-parametric regression models for compositional response data with zeros". Statistics and Computing, 33(106). <doi:10.1007/s11222-023-10277-5>. n) Tsagris. M. (2025). "Constrained least squares simplicial-simplicial regression". Statistics and Computing, 35(27). <doi:10.1007/s11222-024-10560-z>. o) Sevinc V. and Tsagris. M. (2024). "Energy Based Equality of Distributions Testing for Compositional Data". <doi:10.48550/arXiv.2412.05199>.

Nonfree firmware for Realtek ethernet, wifi, and Bluetooth chips. This package contains nonfree firmware for the following chips:

• Realtek RTL8188EE firmware (rtlwifi/rtl8188efw.bin)

• Realtek RTL8188EU firmware (rtlwifi/rtl8188eufw.bin)

• Realtek RTL8192CE/RTL8188CE firmware (rtlwifi/rtl8192cfw.bin)

• Realtek RTL8192CE/RTL8188CE B-cut firmware (rtlwifi/rtl8192cfwU_B.bin)

• Realtek RTL8188CE A-cut firmware, version 4.816.2011 (rtlwifi/rtl8192cfwU.bin)

• Realtek RTL8192CU/RTL8188CU UMC A-cut firmware (rtlwifi/rtl8192cufw_A.bin)

• Realtek RTL8192CU/RTL8188CU UMC B-cut firmware (rtlwifi/rtl8192cufw_B.bin)

• Realtek RTL8192CU/RTL8188CU TMSC firmware (rtlwifi/rtl8192cufw_TMSC.bin)

• Realtek RTL8192CU/RTL8188CU fallback firmware (rtlwifi/rtl8192cufw.bin)

• Realtek RTL8192DE firmware (rtlwifi/rtl8192defw.bin)

• Realtek RTL8192EE wifi firmware (rtlwifi/rtl8192eefw.bin)

• Realtek RTL8192EU non-WoWLAN firmware (rtlwifi/rtl8192eu_nic.bin)

• Realtek RTL8192EU WoWLAN firmware (rtlwifi/rtl8192eu_wowlan.bin)

• Realtek RTL8192SE/RTL8191SE firmware, version 4.816.2011 (rtlwifi/rtl8192sefw.bin)

• Realtek RTL8192SU/RTL8712U firmware (rtlwifi/rtl8712u.bin)

• Realtek RTL8723AU rev A wifi-with-BT firmware (rtlwifi/rtl8723aufw_A.bin)

• Realtek RTL8723AU rev B wifi-with-BT firmware (rtlwifi/rtl8723aufw_B.bin)

• Realtek RTL8723AU rev B wifi-only firmware (rtlwifi/rtl8723aufw_B_NoBT.bin)

• Realtek RTL8723BE firmware, version 36 (rtlwifi/rtl8723befw_36.bin)

• Realtek RTL8723BE firmware (rtlwifi/rtl8723befw.bin)

• Realtek RTL8723BS BT firmware (rtlwifi/rtl8723bs_bt.bin)

• Realtek RTL8723BS wifi non-WoWLAN firmware (rtlwifi/rtl8723bs_nic.bin)

• Realtek RTL8723BS wifi WoWLAN firmware (rtlwifi/rtl8723bs_wowlan.bin)

• Realtek RTL8723BU non-WoWLAN firmware (rtlwifi/rtl8723bu_nic.bin)

• Realtek RTL8723BU WoWLAN firmware (rtlwifi/rtl8723bu_wowlan.bin)

• Realtek RTL8723DE firmware (rtlwifi/rtl8723defw.bin)

• Realtek RTL8723AE rev B firmware (rtlwifi/rtl8723fw_B.bin)

• Realtek RTL8723AE rev A firmware (rtlwifi/rtl8723fw.bin)

• Realtek RTL8821AE firmware, version 29 (rtlwifi/rtl8821aefw_29.bin)

• Realtek RTL8821AE firmware (rtlwifi/rtl8821aefw_wowlan.bin)

• Realtek RTL8821AE firmware (rtlwifi/rtl8821aefw.bin)

• Realtek RTL8822BE firmware (rtlwifi/rtl8822befw.bin)

• Realtek RTL8105E-1 firmware (rtl_nic/rtl8105e-1.fw)

• Realtek RTL8106E-1 firmware, version 0.0.1 (rtl_nic/rtl8106e-1.fw)

• Realtek RTL8106E-2 firmware, version 0.0.1 (rtl_nic/rtl8106e-2.fw)

• Realtek RTL8107E-1 firmware, version 0.0.2 (rtl_nic/rtl8107e-1.fw)

• Realtek RTL8107E-2 firmware, version 0.0.2 (rtl_nic/rtl8107e-2.fw)

• Realtek RTL8111D-1/RTL8168D-1 firmware (rtl_nic/rtl8168d-1.fw)

• Realtek RTL8111D-2/RTL8168D-2 firmware (rtl_nic/rtl8168d-2.fw)

• Realtek RTL8168E-1 firmware (rtl_nic/rtl8168e-1.fw)

• Realtek RTL8168E-2 firmware (rtl_nic/rtl8168e-2.fw)

• Realtek RTL8168E-3 firmware, version 0.0.4 (rtl_nic/rtl8168e-3.fw)

• Realtek RTL8168F-1 firmware, version 0.0.5 (rtl_nic/rtl8168f-1.fw)

• Realtek RTL8168F-2 firmware, version 0.0.4 (rtl_nic/rtl8168f-2.fw)

• Realtek RTL8168G-1 firmware, version 0.0.3 (rtl_nic/rtl8168g-1.fw)

• Realtek RTL8168G-2 firmware, version 0.0.1 (rtl_nic/rtl8168g-2.fw)

• Realtek RTL8168G-3 firmware, version 0.0.1 (rtl_nic/rtl8168g-3.fw)

• Realtek RTL8168H-1 firmware, version 0.0.2 (rtl_nic/rtl8168h-1.fw)

• Realtek RTL8168H-2 firmware, version 0.0.2 (rtl_nic/rtl8168h-2.fw)

• Realtek RTL8402-1 firmware, version 0.0.1 (rtl_nic/rtl8402-1.fw)

• Realtek RTL8411-1 firmware, version 0.0.3 (rtl_nic/rtl8411-1.fw)

• Realtek RTL8411-2 firmware, version 0.0.1 (rtl_nic/rtl8411-2.fw)

• Realtek RTL8192EE Bluetooth firmware (rtl_bt/rtl8192ee_fw.bin)

• Realtek RTL8812AE Bluetooth firmware (rtl_bt/rtl8812ae_fw.bin)

• Realtek RTL8761A Bluetooth firmware (rtl_bt/rtl8761a_fw.bin)

• Realtek RTL8821A Bluetooth firmware (rtl_bt/rtl8821a_fw.bin)

• Realtek RTL8192EU Bluetooth firmware (rtl_bt/rtl8192eu_fw.bin)

• Realtek RTL8723AU rev A Bluetooth firmware (rtl_bt/rtl8723a_fw.bin)

• Realtek RTL8723BU rev B Bluetooth firmware (rtl_bt/rtl8723b_fw.bin)

• Realtek RTL8723D Bluetooth config (rtl_bt/rtl8723d_config.bin)

• Realtek RTL8723D Bluetooth firmware (rtl_bt/rtl8723d_fw.bin)

• Realtek RTL8821C Bluetooth config (rtl_bt/rtl8821c_config.bin)

• Realtek RTL8821C Bluetooth firmware (rtl_bt/rtl8821c_fw.bin)

• Realtek RTL8822B Bluetooth config (rtl_bt/rtl8822b_config.bin)

• Realtek RTL8822B Bluetooth firmware (rtl_bt/rtl8822b_fw.bin)

• Realtek RTL8822CU Bluetooth firmware (rtl_bt/rtl8822cu_fw.bin)

This package provides traits to implement custom Runtimes.

Simple work-stealing parallelism for Rust - fork for rustc

Derive macro for rkyv

Derive macro for rkyv

This package provides an automated way to track, and then re-run failed RSpec tests.

Collection of portable choice dialog widgets.

Rust bindings for the Graphene library.

Rust bindings for the Graphene library.

This package provides Generic range allocator.

This package provides type checked partial references.

This package performs exact rate ratio tests.