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>.

This package contains more modern tools for causal inference using regression standardization. Four general classes of models are implemented; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models, and shared frailty gamma-Weibull models. Methodological details are described in Sjölander, A. (2016) <doi:10.1007/s10654-016-0157-3>. Also includes functionality for doubly robust estimation for generalized linear models in some special cases, and the ability to implement custom models.

Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) <doi:10.1080/02664763.2022.2054962> for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data.

This package provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) <doi:10.48550/arxiv.2110.12336>. A detailed vignette is included.

This package provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions.

This package provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.

This package provides a spatio-dynamic modelling package that focuses on three characteristic wetland plant communities in a semiarid Mediterranean wetland in response to hydrological pressures from the catchment. The package includes the data on watershed hydrological pressure and the initial raster maps of plant communities but also allows for random initial distribution of plant communities. For more detailed info see: Martinez-Lopez et al. (2015) <doi:10.1016/j.ecolmodel.2014.11.024>.

This package implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) <https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) <https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979) <doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, Yuce (2008) <https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <doi:10.48550/arXiv.0810.2124>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) <doi:10.2307/1912934>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016) <doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017) <doi:10.1080/00949655.2016.1198906>.

This package provides methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (â Equity Asset Valuation (4th Edition)â , 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).

This package provides an implementation of simplicial complexes for Topological Data Analysis (TDA). The package includes functions to compute faces, boundary operators, Betti numbers, Euler characteristic, and to construct simplicial complexes. It also implements persistent homology, from building filtrations to computing persistence diagrams, with the aim of helping readers understand the core concepts of computational topology. Methods are based on standard references in persistent homology such as Zomorodian and Carlsson (2005) <doi:10.1007/s00454-004-1146-y> and Chazal and Michel (2021) <doi:10.3389/frai.2021.667963>.

Summary ellipses superimposed on a scatter plot contain all bi-variate summary statistics for regression analysis. Furthermore, the outer ellipse flags potential outliers. Multiple groups can be compared in terms of centers and spreads as illustrated in the examples.

This package provides a general-purpose implementation of synthetic control methods that accounts for potential spillover effects between units. Based on the methodology of Cao and Dowd (2019) <doi:10.48550/arXiv.1902.07343> "Estimation and Inference for Synthetic Control Methods with Spillover Effects".

This package implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (JASA 2020) and Kolyan Ray, Botond Szabo, and Gabriel Clara (NeurIPS 2020).

Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models. All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.

This package provides a pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a frames argument to target any subset of frames, bridging the gap between magick functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via ImageMagick Studio LLC (2024) <https://imagemagick.org>.

Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.

The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough.

Sample surveys use scientific methods to draw inferences about population parameters by observing a representative part of the population, called sample. The SRSWOR (Simple Random Sampling Without Replacement) is one of the most widely used probability sampling designs, wherein every unit has an equal chance of being selected and units are not repeated.This function draws multiple SRSWOR samples from a finite population and estimates the population parameter i.e. total of HT, Ratio, and Regression estimators. Repeated simulations (e.g., 500 times) are used to assess and compare estimators using metrics such as percent relative bias (%RB), percent relative root means square error (%RRMSE).For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" <https://archive.org/details/samplingtechniqu0000coch_t4x6>.

This package provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

This package provides functions to calculate EBLUPs (Empirical Best Linear Unbiased Predictor) estimators and their MSEs (Mean Squared Errors). Estimators are based on an area-level linear mixed model introduced by Rao and Yu (1994) <doi:10.2307/3315407>. The REML (Residual Maximum Likelihood) method is used for fitting the model.

Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) <doi:10.4404/hystrix-24.1-6299>. Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. tuneR and seewave packages), geometric morphometrics (i.e. geomorph package) and multivariate analysis (e.g. stats package). For more information, please see Rocha and Romano (2021) and check SoundShape repository on GitHub for news and updates <https://github.com/p-rocha/SoundShape>.

Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.

This package provides tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.

The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.