Single-Index Quantile Regression is effective in some scenarios. We provides functions that allow users to fit Single-Index Quantile Regression model. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) <doi:10.1016/j.jmva.2010.02.003> for details.

Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". . This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). . As an alternative, pre-built Docker containers such as the Rocker container r-devel-san or the multi-purpose container r-debug can be used. . This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. . The code samples are based on the examples from the Address Sanitizer Wiki at <https://github.com/google/sanitizers/wiki>.

The goal of siteymlgen is to make it easy to organise the building of your R Markdown website. The init() function placed within the first code chunk of the index.Rmd file of an R project directory will initiate the generation of an automatically written _site.yml file. siteymlgen recommends a specific naming convention for your R Markdown files. This naming will ensure that your navbar layout is ordered according to a hierarchy.

Identifies what optimal subset of a desired number of items should be retained in a short version of a psychometric instrument to assess the â broadestâ proportion of the construct-level content of the set of items included in the original version of the said psychometric instrument. Expects a symmetric adjacency matrix as input (undirected weighted network model). Supports brute force and simulated annealing combinatorial search algorithms.

Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.

Generate knockoffs for genetic data and hidden Markov models. For more information, see the website below and the accompanying papers: "Gene hunting with hidden Markov model knockoffs", Sesia et al., Biometrika, 2019, (<doi:10.1093/biomet/asy033>). "Multi-resolution localization of causal variants across the genome", Sesia et al., bioRxiv, 2019, (<doi:10.1101/631390>).

This package provides a facility to generate balanced semi-Latin rectangles with any cell size (preferably up to ten) with given number of treatments, see Uto, N.P. and Bailey, R.A. (2020). "Balanced Semi-Latin rectangles: properties, existence and constructions for block size two". Journal of Statistical Theory and Practice, 14(3), 1-11, <doi:10.1007/s42519-020-00118-3>. It also provides facility to generate partially balanced semi-Latin rectangles for cell size 2, 3 and 4 for any number of treatments.

This package provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the mapproj package.

Access functionality of the heatmaply package through Shiny UI'.

The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) based on the Fisher's (1936 <doi:10.1111/j.1469-1809.1936.tb02137.x>) "discriminant function" Dabholkar (1999 <https://books.google.co.in/books?id=mlFtumAXQ0oC&lpg=PA4&ots=Xgxp1qLuxS&dq=elements%20of%20biometrical%20genetics&lr&pg=PP1#v=onepage&q&f=false>). In this package selection index is calculated based on the Smith (1937) selection index method.

This package provides a bridge is created between existing robust open-source record linkage algorithms and an urgently needed user-friendly platform that removes financial and technical barriers, setting a new standard for data interoperability in public health and bioinformatics. The fastLink algorithms are used for matching. Ted Enamorado et al. (2019) <doi:10.1017/S0003055418000783>.

Add fancy CSS effects to your shinydashboards or shiny apps. 100% compatible with shinydashboardPlus and bs4Dash'.

Perform common dendrometry operations such as inventory preparing, and inventory data analysis.

We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns.

Survey to collect data about the social and economic conditions of Indonesian society. This activity aims to include: As a data source for planning and evaluating national, sectoral development programs, and providing indicators for Sustainable Development Goals (TPB), National Medium Term Development Plan (RPJMN), and Nawacita, GDP/GRDP and annual Integrated Institutional Balance Sheet.

Quantify stratigraphic disorder using the metrics defined by Burgess (2016) <doi:10.2110/jsr.2016.10>. Contains a range of utility tools to construct and manipulate stratigraphic columns.

This package provides a computational framework for identification of B cell clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main functions are included (identicalClones, hierarchicalClones, and spectralClones) that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the same V gene, J gene and junction length. Nouri N and Kleinstein SH (2018) <doi: 10.1093/bioinformatics/bty235>. Nouri N and Kleinstein SH (2019) <doi: 10.1101/788620>. Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.

Reference data sets of species sensitivities to compare the results of fitting species sensitivity distributions using software such as ssdtools and Burrlioz'. It consists of 17 primary data sets from four different Australian and Canadian organizations as well as five datasets from anonymous sources. It also includes a data set of the results of fitting various distributions using different software.

Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at <https://barryzee.github.io/Concordance/index.html>.

Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.

This package provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).

This package provides functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.

Performance evaluation metrics for supervised and unsupervised machine learning, statistical learning and artificial intelligence applications. Core computations are implemented in C++ for scalability and efficiency.

This package performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 <https://www.uv.es/revispsi/articulos3.00/ferran7.pdf>).