This is an archive of bid tabulations before June 2016. For current bid tabulations see https://datacatalog.cookcountyil.gov/d/32au-zaqn. The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

For bid tabulations before June 2016 see https://datacatalog.cookcountyil.gov/d/pn38-yupm

Analysis of ‘Procurement - Bid Tabulations’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b216b480-9ce2-4d8c-85b6-90d5ce894258 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

For bid tabulations before June 2016 see https://datacatalog.cookcountyil.gov/d/pn38-yupm

--- Original source retains full ownership of the source dataset ---

The SLO_BIDDING_DATA dataset includes data regarding tendered bids, accepted bids and price of capacity bids (monthly aggregates). It is intended for market players and system operators to follow the prices on the market and amount of purchased flexibility.

Two data sets in the CIM XML format are available with information about bidding for DSOs. One document is covering the process type "congestion management", the other for "voltage control". Due to CGMES being TSO oriented, some attributes are missing to fully describe DSO processes. Therefore we had to extend the attribute "processType" to cover DSO needs.

Addition information about the Slovenian demo is available in the OneNet 10.4 deliverable (OneNet_D10.4_V1.0.pdf (onenet-project.eu)).

XSD is compliant with ReserveBid_Document defined by ENTSO-E (Reserve bid document UML model and schema (entsoe.eu)).

Description: This dataset includes all bids received through the Flexibility Procurement Dynamic Purchasing System (DPS) Piclo Platform. It is crucial for stakeholders seeking insights into the current market liquidity of DSO Flexibility and researching average bid prices. This dataset complies with Ofgem Licence Condition C31E. Contents: Summary of bid prices offered Bid approval information Reasons for bid rejections Participating Flexibility Providers Purpose: The dataset aims to provide visibility and insight into the SPEN Flexibility Market. It enables users to research average accepted bid prices to inform future market bids. This is part of Licence Condition 31E, which requires DSO companies to publish all competitions and their outcomes after contracting a Flexibility Service. Source: Compiled by the DPS (Piclo) Platform. Date Range: Covers all competitions to date and is updated monthly. License Areas: Includes both SPM and SPD license areas. Granularity: Detailed on a monthly basis, specifying all competition data facilitated on the DPS (Piclo) platform.DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).Example Use CasesFlexibility Service Providers / Energy SuppliersFlexibility service providers could use the data to:Identify flexibility opportunities in the SPEN area by analyzing historical bid prices, participating providers, and the reasons behind accepted or rejected bids.Plan their service offerings and resource allocation based on specified MW capacity, locations, and past competition outcomes.Analyze market signals and trends to optimize their participation in future flexibility markets.Potential Platform ProvidersAs our third-party platform provider, DPS platofrms could use the data to:Provide detailed insights into upcoming flexibility procurement needsHelp FSPs plan and allocate their resources more effectively based on the latest bidding data.Consultancies / Competitor AnalysisConsultancies and competitors can leverage this data to:Identify flexibility opportunities in the SPEN area by analysing historical bid prices, participating providers, and the reasons behind accepted or rejected bids.Research service offerings and resource allocation for their clients based on information provided, such as specified MW capacity, locations, and past competition outcomes.Analyse market signals and trends to identify opportunities for their clients to optimise their knowledge and make informed decisions on DSO Flexibility markets.Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Flexibility Bids, Competitions and Registered Assets dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

The "Flexibility Bids" dataset includes all bids received through the Flexibility Procurement Dynamic Purchasing System (DPS) Piclo Platform. It is crucial for stakeholders seeking insights into the current market liquidity of DSO Flexibility and researching average bid prices. This dataset complies with Ofgem Licence Condition C31E.Contents:Summary of bid prices offeredBid approval informationReasons for bid rejectionsParticipating Flexibility ProvidersPurpose: The dataset aims to provide visibility and insight into the SPEN Flexibility Market. It enables users to research average accepted bid prices to inform future market bids. This is part of Licence Condition 31E, which requires DSO companies to publish all competitions and their outcomes after contracting a Flexibility Service. DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Flexibility Bids, Competitions and Registered Assets dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

The bid tabulations listed below reflect grand totals only of the valid bids received. They may not reflect any added or deducted alternatives. Evaluations will be made by the project manager or his representative for correct price extensions, meeting specifications, etc. Once those specifications are completed, the department’s governing board or commission will award the bid to the lowest, responsive, responsible bidder meeting the bid’s specifications. The bids listed may only be the top three responsive bids. Please click on the bid number below to review the results from that particular bid.

The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

For bid tabulations before June 2016 see https://datacatalog.cookcountyil.gov/d/pn38-yupm

The submitting agency SHALL be responsible for ensuring the accuracy of the bid document posting(s), closing time, delivery of bid, evaluation, awards and any other procurement documentation. The agency shall not waive any state or federal law or agency’s requirements on purchases.

Bid Management Software for General Contractors Market size was valued at USD 769.57 Million in 2023 and is projected to reach USD 1515.59 Million by 2030, growing at a CAGR of 11.98% during the forecast period 2024-2030.Global Bid Management Software for General Contractors Market DriversThe market drivers for the Bid Management Software for General Contractors Market can be influenced by various factors. These may include:Efficiency and Time Savings: By streamlining the entire bidding process and eliminating manual labor, paperwork, and administrative duties, bid management software helps general contractors save time and run more efficiently.Increased Precision and Accuracy: By providing tools for accurate cost estimation, bid generation, and project documentation, these software programs reduce mistakes and raise the precision of contractor bids.Improved Cooperation and Communication: By offering centralized platforms for communication, document sharing, and real-time updates, bid management software promotes improved coordination during the bidding process among project teams, suppliers, and subcontractors.Competitive Advantage: By presenting polished and thorough bids, exhibiting their expertise, and increasing their chances of getting contracts in a competitive market, general contractors who use bid management software get an advantage over their rivals.Regulatory Compliance: Software that guarantees adherence to industry-specific norms and standards makes it easier to comply with regulatory standards, documentation requirements, and legal components of the bidding process.Improved Bid Success Rate: The software's capacity to produce thorough, accurate, and expert bids raises the likelihood of obtaining contracts, which improves the success rate of landing projects.

Neuroscience studies entail the generation of massive collections of heterogeneous data (e.g. demographics, clinical records, medical images). Integration and analysis of such data in research centers is pivotal for elucidating disease mechanisms and improving clinical outcomes. However, data collection in clinics often relies on non-standardized methods, such as paper-based documentation. Moreover, diverse data types are collected in different departments hindering efficient data organization, secure sharing and compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Henceforth, in this manuscript we present a specialized data management system designed to enhance research workflows in Deep Brain Stimulation (DBS), a state-of-the-art neurosurgical procedure employed to treat symptoms of movement and psychiatric disorders. The system leverages REDCap to promote accurate data capture in hospital settings and secure sharing with research institutes, Brain Imaging Data Structure (BIDS) as image storing standard and a DBS-specific SQLite database as comprehensive data store and unified interface to all data types. A self-developed Python tool automates the data flow between these three components, ensuring their full interoperability. The proposed framework has already been successfully employed for capturing and analyzing data of 107 patients from 2 medical institutions. It effectively addresses the challenges of managing, sharing and retrieving diverse data types, fostering advancements in data quality, organization, analysis, and collaboration among medical and research institutions.

The submitting agency SHALL be responsible for ensuring the accuracy of the bid document posting(s), closing time, delivery of bid, evaluation, awards and any other procurement documentation. The agency shall not waive any state or federal law or agency’s requirements on purchases.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

For bid tabulations before June 2016 see https://datacatalog.cookcountyil.gov/d/pn38-yupm

Bid Management Software Market Outlook

The global bid management software market size was estimated to be USD 1.3 billion in 2023 and is projected to reach USD 3.2 billion by 2032, growing at a CAGR of 10.4% from 2024 to 2032. This substantial growth is driven by the increasing need for efficient and effective bid management processes across various industries, coupled with advancements in software technologies.

Several growth factors are contributing to the expansion of the bid management software market. One of the primary drivers is the rising adoption of digital transformation across various sectors. Organizations are increasingly recognizing the importance of automating and streamlining their bid management processes to enhance productivity and accuracy. Bid management software helps businesses to manage bids more effectively by providing a centralized platform for tracking bid status, deadlines, and documentation, which significantly reduces manual errors and improves efficiency.

Another significant growth factor is the increasing complexity of bids and tenders. As competition intensifies in various industries, bids are becoming more complex, requiring meticulous management and coordination. Bid management software solutions provide the tools and features necessary to handle these complexities, including real-time collaboration, advanced analytics, and customizable templates. These features enable organizations to submit more competitive and well-structured bids, thereby increasing their chances of winning contracts.

The growing emphasis on data-driven decision-making is also fueling the demand for bid management software. Organizations are leveraging data analytics to gain insights into their bid performance, identify trends, and make informed decisions. Bid management software with integrated analytics capabilities allows businesses to analyze historical bid data, track key performance indicators, and measure success rates. This data-driven approach helps organizations to optimize their bid strategies, improve win rates, and enhance overall business growth.

From a regional perspective, the North American market is expected to dominate the bid management software market during the forecast period. This can be attributed to the presence of many leading software providers, high adoption of advanced technologies, and the strong focus on digital transformation in the region. Additionally, the Asia Pacific region is anticipated to witness significant growth due to the rapid industrialization, increasing investments in infrastructure projects, and the growing adoption of cloud-based solutions. Europe is also expected to contribute substantially to the market growth, driven by the increasing demand for efficient bid management solutions in the construction and IT sectors.

Component Analysis

The bid management software market is segmented by components into software and services. The software segment is further categorized into on-premises and cloud-based solutions. The software component holds a significant share of the market as it provides the core functionalities required for bid management. Companies prefer software solutions because they offer features such as automated bid tracking, collaboration tools, and advanced analytics, which streamline the bid process and improve efficiency.

On the other hand, the services segment is also gaining traction, driven by the increasing demand for implementation, training, and support services. Organizations often require assistance in deploying and configuring bid management software to suit their specific needs. Service providers offer comprehensive solutions that include consulting, customization, and ongoing support, ensuring that businesses can fully leverage the capabilities of the software. As the adoption of bid management software continues to grow, the demand for associated services is expected to rise correspondingly.

Cloud-based bid management software is becoming increasingly popular due to its scalability, flexibility, and cost-effectiveness. Cloud solutions eliminate the need for significant upfront investments in hardware and infrastructure, making them an attractive option for small and medium enterprises (SMEs). Additionally, cloud-based solutions offer the advantage of remote access, allowing teams to collaborate and manage bids from anywhere, thereby enhancing productivity and efficiency. The cloud segment is expected to witness robust growth during the forecast period.

In contrast, on-premises bid management software is

The submitting agency SHALL be responsible for ensuring the accuracy of the bid document posting(s), closing time, delivery of bid, evaluation, awards and any other procurement documentation. The agency shall not waive any state or federal law or agency’s requirements on purchases.

The online bidding system is a flexible solution for supporting lot- based online bidding. The thesis explains the construction of a bidding website. The system has been designed to be highly scalable and capable of supporting large numbers of bidders in inactive bidding. The online bidding system lets you easily browse lots and place bids using a secure server. All cost of mailing lots will be paid by the buyer. The objective is to develop a user-friendly bidding site where any kind of product can be bid and

The City of Boston reports on our contract awards as part of our commitment to transparency and to growing pathways to equitable procurement for diverse suppliers. This dataset reports on our procurement contracts and other agreements, such as grants, from Fiscal Year 2019 through Fiscal Year 2025, Quarter 3.

A contract is an agreement between the City of Boston and a supplier to acquire supplies and services over $10,000. Contracts have an award value. This is the maximum dollar amount that a business can be paid, in total, over the life of the contract.

You can view an interactive dashboard of our procurement contract award at https://www.boston.gov/equitable-procurement. This dashboard does not report on non-procurement contracts or agreement types.

To learn more about the City's procurement and supplier diversity efforts, visit:

• https://www.boston.gov/procurement
• https://www.boston.gov/supplier-diversity

** Notes on the Data **

• In some circumstances, the City department name was not recorded on the electronic contract record.
• There are some instances where the description of the contract only references City identifiers, such as a bid or purchase order number.
• Some electronic contract records do not have information recorded in our administrative systems in order to accurately categorize them as a Procurement Contract. We have included these contracts as "Other Contracts".
• In the event of a discrepancy between the electronic contract record and the signed contract document, the signed contract document is the record of truth. There are some instances where these two may differ.

** Contract Categorization **

The City categorizes agreements into Procurement Contracts and Other Contracts to distinguish contracts entered through a procurement process from other types of agreements.

• Procurement Contract: An agreement between the City of Boston and a supplier to acquire supplies and services over $10,000. Procurement contracts are awarded through competitive or limited competition procurements.

  • Competitive Procurement: A City contract is awarded after a competitive evaluation of quotes, bids, or proposals. The City can either publicly invite suppliers or selectively invite several suppliers to submit a response.
  • Limited Competition Procurement: A City contract for an item or service that is not subject to all of the requirements outlined in Massachusetts public procurement laws. This includes exempt, sole source, and emergency procurements.

• Other Contract: Other types of contracts include Government-to-Government Agreements, Grants to Nonprofits, Loans, Revenue Contracts, Trusts or Gift Agreements, Special Education, Health Insurance, and Employment Agreements.

If you have questions about this data or would like to provide feedback, please use this form: https://docs.google.com/forms/d/10_VGn3OEaa-JA5VJynZ9JKw-I3t0BAzw3ckv8rxVMLc/viewform?edit_requested=true

We present simulation results from a study with The Virtual Brain (TVB). Structural, functional and simulated data have been prepared in accordance with Brain Imaging Data Structure (BIDS) standards and annotated according to the openMINDS metadata framework. This simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) resting-state data, diffusion-weighted MRI (dwMRI), and structural MRI were acquired for 50 healthy adult subjects (18 - 80 years of age, mean 41.24±18.33; 31 females, 19 males) at the Berlin Center for Advanced Imaging, Charité University Medicine, Berlin, Germany. We constructed personalized models from this multimodal data of 50 healthy individuals with TVB. We calculated the optimal parameters on an individual basis that predict multiple empirical features in fMRI and EEG, e.g. dynamic functional connectivity and bimodality in the alpha band power, and analyzed inter-individual differences with respect to optimized parameters and structural as well as functional connectivity in a previous study (Triebkorn et al. 2024). We present this large comprehensive empirical and simulated data set in an annotated and structured format following the BIDS Extension Proposal for computational modeling data. We describe how we processed and converted the diverse data sources to make it reusable. In its current form, this dataset can be reused for further research and provides ready-to-use data at various levels of processing including the thereof inferred brain simulation results for a large data set of healthy subjects with a wide age range.

Overview

This dataset contains raw and pre-processed EEG data from a mobile EEG study investigating the effects of cognitive task demands, motor demands, and environmental complexity on attentional processing (see below for experiment details).

All preprocessing and analysis code is deposited in the code directory. The entire MATLAB pipeline can be reproduced by executing the run_pipeline.m script. In order to run these scripts, you will need to ensure you have the required MATLAB toolboxes and R packages on your system. You will also need to adapt def_local.m to specify local paths to MATLAB and EEGLAB. Descriptive statistics and mixed-effects models can be reproduced in R by running the stat_analysis.R script.

See below for software details.

Citing this dataset

In addition to citing this dataset, please cite the original manuscript reporting data collection and experimental procedures. For more information, see the dataset_description.json file.

License

ODC Open Database License (ODbL). For more information, see the LICENCE file.

Format

Dataset is formatted according to the EEG-BIDS extension (Pernet et al., 2019) and the BIDS extension proposal for common electrophysiological derivatives (BEP021) v0.0.1, which can be found here:

https://docs.google.com/document/d/1PmcVs7vg7Th-cGC-UrX8rAhKUHIzOI-uIOh69_mvdlw/edit#heading=h.mqkmyp254xh6

Note that BEP021 is still a work in progress as of 2021-03-01.

Generally, you can find data in the .tsv files and descriptions in the accompanying .json files.

An important BIDS definition to consider is the "Inheritance Principle" (see 3.5 in the BIDS specification: http://bids.neuroimaging.io/bids_spec.pdf), which states:

Any metadata file (.json, .bvec, .tsv, etc.) may be defined at any directory level. The values from the top level are inherited by all lower levels unless they are overridden by a file at the lower level.

Details about the experiment

Forty-four healthy adults aged 18-40 performed an oddball task involving complex tone (piano and horn) stimuli in three settings: (1) sitting in a quiet room in the lab (LAB); (2) walking around a sports field (FIELD); (3) navigating a route through a university campus (CAMPUS).

Participants performed each environmental condition twice: once while attending to oddball stimuli (i.e. counting the number of presented deviant tones; COUNT), and once while disregarding or ignoring the tone stimuli (IGNORE).

EEG signals were recorded from 32 active electrodes using a Brain Vision LiveAmp 32 amplifier. See manuscript for further details.

MATLAB software details

MATLAB Version: 9.7.0.1319299 (R2019b) Update 5 MATLAB License Number: 678256 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 18363) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode

• MATLAB (v9.7)
• Simulink (v10.0)
• Curve Fitting Toolbox (v3.5.10)
• DSP System Toolbox (v9.9)
• Image Processing Toolbox (v11.0)
• MATLAB Compiler (v7.1)
• MATLAB Compiler SDK (v6.7)
• Parallel Computing Toolbox (v7.1)
• Signal Processing Toolbox (v8.3)
• Statistics and Machine Learning Toolbox (v11.6)
• Symbolic Math Toolbox (v8.4)
• Wavelet Toolbox (v5.3)

The following toolboxes/helper functions were also used:

• EEGLAB (v2019.1)
• ERPLAB (v8.10)
• ICLabel (v1.3)
• clean_rawdata (v2.3)
• bids-matlab-tools (v5.2)
• dipfit (v3.4)
• firfilt (v2.4)
• export_fig (v3.12)
• ColorBrewer (v3.1.0)

R software details

R version 3.6.2 (2019-12-12)

Platform: x86_64-w64-mingw32/x64 (64-bit)

locale: _LC_COLLATE=English_Australia.1252_, _LC_CTYPE=English_Australia.1252_, _LC_MONETARY=English_Australia.1252_, _LC_NUMERIC=C_ and _LC_TIME=English_Australia.1252_

attached base packages:

• stats
• graphics
• grDevices
• utils
• datasets
• methods
• base

other attached packages:

• sjPlot(v.2.8.7)
• emmeans(v.1.5.1)
• car(v.3.0-10)
• carData(v.3.0-4)
• lme4(v.1.1-23)
• Matrix(v.1.2-18)
• data.table(v.1.13.0)
• forcats(v.0.5.0)
• stringr(v.1.4.0)
• dplyr(v.1.0.2)
• purrr(v.0.3.4)
• readr(v.1.4.0)
• tidyr(v.1.1.2)
• tibble(v.3.0.4)
• ggplot2(v.3.3.2)
• tidyverse(v.1.3.0)

loaded via a namespace (and not attached):

• nlme(v.3.1-149)
• pbkrtest(v.0.4-8.6)
• fs(v.1.5.0)
• lubridate(v.1.7.9)
• insight(v.0.12.0)
• httr(v.1.4.2)
• numDeriv(v.2016.8-1.1)
• tools(v.3.6.2)
• backports(v.1.1.10)
• utf8(v.1.1.4)
• R6(v.2.4.1)
• sjlabelled(v.1.1.7)
• DBI(v.1.1.0)
• colorspace(v.1.4-1)
• withr(v.2.3.0)
• tidyselect(v.1.1.0)
• curl(v.4.3)
• compiler(v.3.6.2)
• performance(v.0.5.0)
• cli(v.2.1.0)
• rvest(v.0.3.6)
• xml2(v.1.3.2)
• sandwich(v.3.0-0)
• labeling(v.0.3)
• bayestestR(v.0.7.2)
• scales(v.1.1.1)
• mvtnorm(v.1.1-1)
• digest(v.0.6.25)
• foreign(v.0.8-76)
• minqa(v.1.2.4)
• rio(v.0.5.16)
• pkgconfig(v.2.0.3)
• dbplyr(v.1.4.4)
• rlang(v.0.4.8)
• readxl(v.1.3.1)
• rstudioapi(v.0.11)
• farver(v.2.0.3)
• generics(v.0.0.2)
• zoo(v.1.8-8)
• jsonlite(v.1.7.1)
• zip(v.2.1.1)
• magrittr(v.1.5)
• parameters(v.0.8.6)
• Rcpp(v.1.0.5)
• munsell(v.0.5.0)
• fansi(v.0.4.1)
• abind(v.1.4-5)
• lifecycle(v.0.2.0)
• stringi(v.1.4.6)
• multcomp(v.1.4-14)
• MASS(v.7.3-53)
• plyr(v.1.8.6)
• grid(v.3.6.2)
• blob(v.1.2.1)
• parallel(v.3.6.2)
• sjmisc(v.2.8.6)
• crayon(v.1.3.4)
• lattice(v.0.20-41)
• ggeffects(v.0.16.0)
• haven(v.2.3.1)
• splines(v.3.6.2)
• pander(v.0.6.3)
• sjstats(v.0.18.1)
• hms(v.0.5.3)
• knitr(v.1.30)
• pillar(v.1.4.6)
• boot(v.1.3-25)
• estimability(v.1.3)
• effectsize(v.0.3.3)
• codetools(v.0.2-16)
• reprex(v.0.3.0)
• glue(v.1.4.2)
• modelr(v.0.1.8)
• vctrs(v.0.3.4)
• nloptr(v.1.2.2.2)
• cellranger(v.1.1.0)
• gtable(v.0.3.0)
• assertthat(v.0.2.1)
• xfun(v.0.18)
• openxlsx(v.4.2.2)
• xtable(v.1.8-4)
• broom(v.0.7.1)
• coda(v.0.19-4)
• survival(v.3.2-7)
• lmerTest(v.3.1-3)
• statmod(v.1.4.34)
• TH.data(v.1.0-10)
• ellipsis(v.0.3.1)