This package implements the Principal Components Difference-in-Differences estimators as described in Chan, M. K., & Kwok, S. S. (2022) <doi:10.1080/07350015.2021.1914636>.

This package provides functions for bootstrapping the power of ANOVA designs based on estimated means and standard deviations of the conditions. Please refer to the documentation of the boot.power.anova() function for further details.

This package implements the algorithm of Christensen (2024) <doi:10.1214/22-BA1353> for estimating marginal likelihoods via permutation counting.

This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at <https://www.pathlit.io/docs/cli/>.

This package provides path_chain class and functions, which facilitates loading and saving directory structure in YAML configuration files via config package. The file structure you created during exploration can be transformed into legible section in the config file, and then easily loaded for further usage.

Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. Information related to village potential includes economy, security, health, employment, communication and information, sports, entertainment, development, community empowerment, education, socio-culture, transportation in the village. Information related to challenges includes natural disasters, public health, environmental pollution, social problems and security disturbances that occur in the village.

Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) <doi:10.1111/2041-210X.14076>. This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) <doi:10.1111/jeb.14234>.

Simulation of continuous, correlated high-dimensional data with time to event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression with repeated starts and two-dimensional tuning of the Elastic Net.

Makes it easy to push data to Power BI using R and the Power BI REST APIs (see <https://docs.microsoft.com/en-us/rest/api/power-bi/>). A set of functions for turning data frames into Power BI datasets and refreshing these datasets are provided. Administrative tasks such as monitoring refresh statuses and pulling metadata about workspaces and users are also supported.

This package implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.

Obtener listado de datos, acceder y extender series del Portal de Datos de Hacienda.Las proyecciones se realizan con forecast', Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>. Search, download and forecast time-series from the Ministry of Economy of Argentina. Forecasts are built with the forecast package, Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>.

This package produces power spectral density estimates through iterative refinement of the optimal number of sine-tapers at each frequency. This optimization procedure is based on the method of Riedel and Sidorenko (1995), which minimizes the Mean Square Error (sum of variance and bias) at each frequency, but modified for computational stability. The same procedure can now be used to calculate the cross spectrum (multivariate analyses).

Simulate and run the Gaussian puff forward atmospheric model in sensor (specific sensor coordinates) or grid (across the grid of a full oil and gas operations site) modes, following Jia, M., Fish, R., Daniels, W., Sprinkle, B. and Hammerling, D. (2024) <doi:10.26434/chemrxiv-2023-hc95q-v3>. Numerous visualization options, including static and animated, 2D and 3D, and a site map generator based on sensor and source coordinates.

Analysis of terms in linear, generalized and mixed linear models, on the basis of multiple comparisons of factor contrasts. Specially suited for the analysis of interaction terms.

This package provides methods for fitting point processes with parameters of generalised additive model (GAM) form are provided. For an introduction to point processes see Cox, D.R & Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.

Displays provenance graphically for provenance collected by the rdt or rdtLite packages, or other tools providing compatible PROV JSON output. The exact format of the JSON created by rdt and rdtLite is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>. More information about rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.

This package provides functions to estimate statistical errors of phylogenetic metrics particularly to detect binary trait influence on diversification, as well as a function to simulate trees with fixed number of sampled taxa and trait prevalence.

Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU.

An implementation of the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020) <doi:10.1214/19-STS732>. Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. Package allows users to recover power, sample size requirements, or minimum detectable effect sizes. Package also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.

Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.

Improving graphics by ameliorating order effects, using Eulerian tours and Hamiltonian decompositions of graphs. References for the methods presented here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.

The Proton Game is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. You have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. The knowledge of dplyr is not required but may be very helpful. This game is linked with the ,,Pietraszko's Cave story available at http://biecek.pl/BetaBit/Warsaw. It's a part of Beta and Bit series. You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.

We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.

This package provides adds postfix and infix logic operators for if, then, unless, and otherwise.