Density, distribution function, quantile function, and random generating function of the Unit-Garima distribution based on Ayuyuen, S., & Bodhisuwan, W. (2024)<doi:10.18187/pjsor.v20i1.4307>.

This package provides convenience functions for user experience research with an emphasis on quantitative user experience testing and reporting. The functions are designed to translate statistical approaches to applied user experience research.

This package provides a classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review).

This package provides functions for the creation and manipulation of scenes and objects within the Unity 3D video game engine (<https://unity.com/>). Specific focuses include the creation and import of terrain data and GameObjects as well as scene management.

This package implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.

User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.

Changes the column names of the inputted dataset to the correct names from the Uniform Crime Report codebook for the "Offenses Known and Clearance by Arrest" datasets from 1998-2014.

This package provides a collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.

Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). The details of the indices in this package can be found in: J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>, T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>, C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>, D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>, J. C. Dunn (1973) <doi:10.1080/01969727308546046>, F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>, M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>, S. H. Kwon (1998) <doi:10.1049/EL:19981523>, S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>, G. W. Miligan (1980) <doi:10.1007/BF02293907>, M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>, M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>, S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>, A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>, Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>, N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>, N. Wiroonsri, O. Preedasawakul (2023) <doi:10.48550/arXiv.2308.14785>, C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495>, X. Xie, G. Beni (1991) <doi:10.1109/34.85677> and Rousseeuw (1987) and Kaufman and Rousseeuw(2009) <doi:10.1016/0377-0427(87)90125-7> and <doi:10.1002/9780470316801> C. Alok. (2010).

Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data.

Consistent with knitr syntax highlighting, usedthese adds a summary table of package & function usage to a Quarto document and enables aggregation of usage across a website.

Fit Bayesian hierarchical models of animal abundance and occurrence via the rstan package, the R interface to the Stan C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class glmnet', so that all of the methods for glmnet are available. See Chatterjee, Hastie and Tibshirani (2025) <doi:10.1162/99608f92.c79ff6db> for details.

Despite there being a section in RFC 7231 <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested structure for User-Agent headers this data is notoriously difficult to parse consistently. Tools are provided that will take in user agent strings and return structured R objects. This is a V8'-backed package based on the ua-parser project <https://github.com/ua-parser>.

Analyzes longitudinal data of HIV decline in patients on antiretroviral therapy using the canonical biphasic exponential decay model (pioneered, for example, by work in Perelson et al. (1997) <doi:10.1038/387188a0>; and Wu and Ding (1999) <doi:10.1111/j.0006-341X.1999.00410.x>). Model fitting and parameter estimation are performed, with additional options to calculate the time to viral suppression. Plotting and summary tools are also provided for fast assessment of model results.

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or self-defined penalties on the B-spline coefficients.

Unit-Gompertz density, cumulative distribution, quantile functions and random deviate generation of the unit-Gompertz distribution. In addition, there are a function for fitting the Generalized Additive Models for Location, Scale and Shape.

Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the MIDAS ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.

Format text (bold, italic, ...) and numbers using UTF-8. Offers functions to search for emojis and include them in your text.

An implementation of Lind and Mehlum's (2010) <doi:10.1111/j.1468-0084.2009.00569.x> Utest to test for the presence of a U shaped or inverted U shaped relationship between variables in (generalized) linear models. It also implements a test of upward/downward sloping relationships at the lower and upper boundary of the data range.

Forms a query to submit for US Treasury yield curve data, posting this query to the US Treasury web site's data feed service. By default the download includes data yield data for 12 products from January 1, 1990, some of which are NA during this span. The caller can pass parameters to limit the query to a certain year or year and month, but the full download is not especially large. The download data from the service is in XML format. The package's main function transforms that XML data into a numeric data frame with treasury product items (constant maturity yields for 12 kinds of bills, notes, and bonds) as columns and dates as row names. The function returns a list which includes an item for this data frame as well as query-related values for reference and the update date from the service.

This package performs a test for second-order stationarity of time series based on unsystematic sub-samples.

The uc.check() function checks whether the roots of a given polynomial lie outside the Unit circle. You can also easily draw an unit circle.