Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package limma (Ritchie et al. (2015), <doi:10.1093/nar/gkv007> ) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).

This package performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Functions that use isotonic regression in the first stage of binning process have an additional feature for correction of minimum percentage of observations and minimum target rate per bin. Additionally, monotonic trend can be identified based on raw data or, if known in advance, forced by functions argument. Missing values and other possible special values are treated separately from so-called complete cases.

This package is deprecated. Please use redatamx instead. Provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

This package provides a comprehensive suite of tools for analyzing Pakistan's Quarterly National Accounts data. Users can gain detailed insights into Pakistan's economic performance, visualize quarterly trends, and detect patterns and anomalies in key economic indicators. Compare sector contributionsâ including agriculture, industry, and servicesâ to understand their influence on economic growth or decline. Customize analyses by filtering and manipulating data to focus on specific areas of interest. Ideal for policymakers, researchers, and analysts aiming to make informed, data-driven decisions based on timely and detailed economic insights.

It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes the solutions to the model, plots of the solution, a summary of the features of the model, a function that covers different types of consumption preferences, and a function that computes the moments of a Markov process. Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>, Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>, Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).

Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>.

This package provides functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money. Raymond M. Brooks (â Financial Management,â 2018, ISBN: 9780134730417). Sheridan Titman, Arthur J. Keown, John D. Martin (â Financial Management: Principles and Applications,â 2017, ISBN: 9780134417219). Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (â Fundamentals of Corporate Finance,â 2019, ISBN: 9780134735313). S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (â Contemporary Business Mathematics with Canadian Applications,â 2020, ISBN: 9780135285015).

U.S. Department of the Treasury, Alcohol and Tobacco Tax and Trade Bureau (TTB) collects data and reports on monthly beer industry production and operations. This data package includes a collection of 10 years (2006 - 2015) worth of data on materials used at U.S. breweries in pounds reported by the Brewer's Report of Operations and the Quarterly Brewer's Report of Operations forms, ready for data analysis. This package also includes historical tax rates on distilled spirits, wine, beer, champagne, and tobacco products as individual data sets.

This package provides a fast JSON parser, generator and validator which converts JSON', NDJSON (Newline Delimited JSON') and GeoJSON (Geographic JSON') data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON'. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from <https://github.com/ibireme/yyjson>.

This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF.

SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006).

Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures.

Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with unknown variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a specified number. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval is proposed by Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

This package contains the CONCOR (CONvergence of iterated CORrelations) algorithm and a series of supplemental functions for easy running, plotting, and blockmodeling. The CONCOR algorithm is used on social network data to identify network positions based off a definition of structural equivalence; see Breiger, Boorman, and Arabie (1975) <doi:10.1016/0022-2496(75)90028-0> and Wasserman and Faust's book Social Network Analysis: Methods and Applications (1994). This version allows multiple relationships for the same set of nodes and uses both incoming and outgoing ties to find positions.

Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). <doi:10.48550/arXiv.2206.09639>.

Estimation tools for multidimensional Gaussian means using empirical Bayesian g-modeling. Methods are able to handle fully observed data as well as left-, right-, and interval-censored observations (Tobit likelihood); descriptions of these methods can be found in Barbehenn and Zhao (2023) <doi:10.48550/arXiv.2306.07239>. Additional, lower-level functionality based on Kiefer and Wolfowitz (1956) <doi:10.1214/aoms/1177728066> and Jiang and Zhang (2009) <doi:10.1214/08-AOS638> is provided that can be used to accelerate many empirical Bayes and nonparametric maximum likelihood problems.

This package provides implementation of statistical methods for random objects lying in various metric spaces, which are not necessarily linear spaces. The core of this package is Fréchet regression for random objects with Euclidean predictors, which allows one to perform regression analysis for non-Euclidean responses under some mild conditions. Examples include distributions in 2-Wasserstein space, covariance matrices endowed with power metric (with Frobenius metric as a special case), Cholesky and log-Cholesky metrics, spherical data. References: Petersen, A., & Müller, H.-G. (2019) <doi:10.1214/17-AOS1624>.

This package provides a mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138> to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) <doi:10.1093/biomet/asz024>); furthermore, we extend it to tackle datasets in metric spaces.

Spatio-temporal locations of an animal are computed from annotated data with a hidden Markov model via particle filter algorithm. The package is relatively robust to varying degrees of shading. The hidden Markov model is described in Movement Ecology - Rakhimberdiev et al. (2015) <doi:10.1186/s40462-015-0062-5>, general package description is in the Methods in Ecology and Evolution - Rakhimberdiev et al. (2017) <doi:10.1111/2041-210X.12765> and package accuracy assessed in the Journal of Avian Biology - Rakhimberdiev et al. (2016) <doi:10.1111/jav.00891>.

Generates a file, containing the main scientific references, prepared to be automatically inserted into an academic paper. The articles present in the list are chosen from the main references generated, by function principal_lister(), of the package bibliorefer'. The generated file contains the list of metadata of the principal references in BibTex format. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>. Hamid DerviÅ . (2019) <doi:10.5530/jscires.8.3.32>.

Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.

Efficiently manage and process data from oTree experiments. Import oTree data and clean them by using functions that handle messy data, dropouts, and other problematic cases. Create IDs, calculate the time, transfer variables between app data frames, and delete sensitive information. Review your experimental data prior to running the experiment and automatically generate a detailed summary of the variables used in your oTree code. Information on oTree is found in Chen, D. L., Schonger, M., & Wickens, C. (2016) <doi:10.1016/j.jbef.2015.12.001>.

This is designed for use with an arbitrary set of equations with an arbitrary set of unknowns. The user selects "fixed" values for enough unknowns to leave as many variables as there are equations, which in most cases means the system is properly defined and a unique solution exists. The function, the fixed values and initial values for the remaining unknowns are fed to a nonlinear backsolver. The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.

This package provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks.