Loading the Korea Labor Institute's KLIPS (Korea Labor & Income Panel Study) panel data and returning data frames. Users must download 26 years of panel data from the Korea Labor Institute website and save it in a folder in an appropriate path. Afterwards, users can easily convert the data into a data frame using this package.

The US Census Bureau provides a seasonal adjustment program now called X-13ARIMA-SEATS building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.

The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.

Byebug is a Ruby 2 debugger implemented using the Ruby 2 TracePoint C API for execution control and the Debug Inspector C API for call stack navigation. The core component provides support that front-ends can build on. It provides breakpoint handling and bindings for stack frames among other things and it comes with a command line interface.

This package includes gene set collections that are used for the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. It includes Human and Mouse versions of the MSidDB (Subramanian, et al. (2005) PNAS, 102(43):15545-15550) and GeneSetDB (Araki, et al. (2012) FEBS Open Bio, 2:76-82) collections.

This package provides an interface to any collection of data sets within a single iSEE web-application. The main functionality of this package is to define a custom landing page allowing app maintainers to list a custom collection of data sets that users can selected from and directly load objects into an iSEE web-application.

This package provides a fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs.

survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types).

Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.

This package provides functions to prepare tidy objects from estimated models via BVAR (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and visualisation thereof. Bridges the gap between estimating models with BVAR and plotting the results in a more sophisticated way with ggplot2 as well as passing them on in a tidy format.

This package provides spatial data for mapping Brunei, including boundaries for districts, mukims, and kampongs, as well as locations of key infrastructure such as masjids, hospitals, clinics, and schools. The package supports researchers, analysts, and developers working with Bruneiâ s geographic and demographic data, offering a quick and accessible foundation for creating maps and conducting spatial studies.

This package contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).

Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps non-linear. This package implements methods for understanding the division of space between the groups.

Access travel times from every US census block to nearby points of interest, by walking, biking, and public transit, over the Close API. Wraps the metered, read-only public endpoints with keyset pagination, ETag/304 conditional requests, token accounting, typed RFC 9457 error handling, and tabular output by default ('sf where geometry applies, a data frame otherwise).

This hosts the findRFM function which generates RFM scores on a 1-5 point scale for customer transaction data. The function consumes a data frame with Transaction Number, Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes. The function returns a data frame with RFM data for the sales information.

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

This package implements cost-benefit analysis primitives from HM Treasury Green Book guidance (HM Treasury, 2022, 2026): the kinked Social Time Preference Rate (STPR), discount factors, net present value (NPV), equivalent annual cost, and real-terms rebasing using the GDP deflator. Designed for UK central government appraisal and evaluation. Bundled parameter tables carry vintage metadata for reproducibility.

Mapping tools that convert place names to coordinates on the fly. These ggplot2 extensions make maps from a data frame where one of the columns contains place names, without having to directly work with the underlying geospatial data and tools. The corresponding map data must be registered with cartographer either by the user or by another package.

Package that simplifies the use of the HPZone API. Most of the annoying and labor-intensive parts of the interface are handled by wrapper functions. Note that the API and its details are not publicly available. Information can be found at <https://www.ggdghorkennisnet.nl/groep/726-platform-infectieziekte-epidemiologen/documenten/map/9609> for those with access.

This package implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. Also implements an algorithm for calculating the thermodynamic (and psychrometric) wet-bulb (and ice-bulb) temperature.

Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.

Normalize data to minimize the difference between sample plates (batch effects). For given data in a matrix and grouping variable (or plate), the function normn_MA normalizes the data on MA coordinates. More details are in the citation. The primary method is Multi-MA'. Other fitting functions on MA coordinates can also be employed e.g. loess.

Local linear estimation of psychometric functions. Provides functions for nonparametric estimation of a psychometric function and for estimation of a derived threshold and slope, and their standard deviations and confidence intervals.For more details see Zychaluk and Foster (2009) <doi:10.3758/APP.71.6.1414> and Foster and Zychaluk (2007) <doi:10.1109/MSP.2007.4286564>.