Version 2.4.1 (DA)

• Corrected the centring of the studentized bootstrap in sharpeTesting() and msharpeTesting(). Both replaced the observed difference by Δ̂ - d and then centred the bootstrap draws on that quantity, so the reference distribution was shifted by the hypothesised value d instead of being centred on the estimate. The test statistic itself was correct, so the default d = 0 was never affected; but the automatic block-length selectors pass the observed difference as d, so every rejection rate in their calibration loop was computed from a mis-centred distribution and bBoot = 0 returned an essentially arbitrary block length. The draws are now centred on the estimate
• The bias correction of the true-null proportion is scaled by the number of valid comparisons rather than by the number of columns. Appending peer columns that were entirely missing changed the estimate even though the valid comparisons were identical. A within-universe screening now appends a single placeholder so that the published convention of Ardia and Boudt (2018) is preserved in the correction without counting unavailable peers
• minObsPi is enforced on each focal fund’s final count of valid peers, in the cross-group path as well as the within-group one; cross-group screening previously ignored it, and unbalanced panels could give results that depended on the order of the columns
• alphaTesting() estimates both reported alphas on the same concordant sample as the difference it tests. They were previously fitted on each fund’s own available observations, so the two reported alphas need not have differed by the reported dalpha
• dsharpe and dmsharpe hold raw ratio differences in all screening paths. With the default ttype = 2 the screenings stored the cross-multiplied statistic under a field named as a difference, while the pairwise tests returned the raw difference – the same name meant two different things. The tests continue to use the product statistic internally
• Fixed the circular block bootstrap when the block length does not divide the number of concordant observations. bootIndices() drew floor(T / bBoot) blocks, so the last T %% bBoot indices of every bootstrap sample kept their initial value of zero; because the callers remap indices with 1 + ids %% T, each of those zeros silently selected the first observation. At T = 50 and bBoot = 6 this over-sampled observation 1 by a factor of about three and biased the bootstrap null distribution, with no error or NA to signal it. The routine now draws ceiling(T / bBoot) blocks and keeps the first T indices. Results are unchanged when bBoot divides T, and the default bBoot = 1 was never affected
• A fund with no usable observation no longer aborts a screening: infoFund() called lm() on every column, so an all-NA fund stopped the whole run with “0 (non-NA) cases” even though the pairwise stage handles such a column correctly. Its summary statistics are now returned as NA
• Non-finite returns are treated as missing. The missingness masks tested only is.na() and is.nan(), so an Inf counted as an observation and the fit then failed with “NA/NaN/Inf in ‘y’”. Inf and NaN are now normalised to NA on entry, and a series containing one gives exactly the result it would give with NA in the same position
• bBoot = 0 (data-driven block length) works on series containing NA in sharpeTesting() and msharpeTesting(): the block-length routines were passed the raw series rather than the concordant observations the test itself uses, so they returned NA and the bootstrap then failed with a subscript error
• A factor set the design cannot fully estimate (collinear or constant factors) no longer aborts screen_beta screening or alphaTesting(). summary(lm) omits inestimable coefficients, so indexing its table by position over-ran it and could read a neighbouring coefficient; coefficients are now matched by name and inestimable ones are reported as NA

Version 2.4.0 (DA)

• New optional control$fastAdjust (default FALSE): the truncated-normal bias correction of pi0 inverts its monotone map for the whole vector at once (vectorised bisection) instead of one uniroot() call per value. This is the dominant cost of a screening with a data-driven lambda – a 100-fund alphaScreening() drops from about 19s to about 4.5s (4.2x). The bisection locates the root to about 1e-12; because uniroot stops at its own tolerance (about 1.2e-4), the two paths typically differ by a few 1e-5, the fast path being the more accurate. The original code path remains the default so that published results reproduce exactly. Bootstrap indices are now built only for the bootstrap test (type = 2), so the asymptotic path no longer depends on bBoot
• Bootstrap screening on unbalanced panels fixed: the bootstrap indices are now pre-generated in the master for every distinct pairwise complete-case length (previously a single full-length index matrix was remapped by modulo inside the workers, which biased the iid resampling and broke the circular-block structure for bBoot > 1); pairs shorter than the block length are left untested; balanced panels are unaffected. All randomness stays in the master, so seeded results do not depend on nCore
• nCore = 1 (the default) now runs serially without creating a PSOCK cluster, removing the per-call cluster overhead (noticeable in rollScreening’s window loop); results are identical to the cluster path
• The symmetric bootstrap p-value of the Sharpe test now uses >=, consistent with the modified Sharpe test (ties have probability zero for continuous returns)
• Documentation: HAC availability in alphaScreening clarified; the gammaPos/gammaNeg counting rules are stated explicitly; misleading “bootstrap and HAC” example headers fixed (hac is ignored when type = 2); summary documents that win/loss counts are within-group only
• Robustness (pre-submission audit): PSOCK clusters are now closed with on.exit() so workers are not leaked on error; processControl requires the count-like controls (nBoot, bBoot, nCore, minObs, minObsPi) to be whole numbers and rejects a bootstrap block length exceeding the sample size; within-group screening and cross-group screening now stop with a clear message on degenerate inputs (a single fund, an empty peer group)
• alphaScreening/alphaTesting now enforce minObs on the factor complete-case sample (factor NAs were previously ignored), and alphaTesting(screen_beta = TRUE, hac = TRUE) now returns the alpha component as a coefficient-by-fund matrix, consistent with the non-HAC path (the print method reports the alpha row)
• confint is now tested for all three ratios (pipos/pizero/pineg)
• Added a confint method for SCREENING objects: nonparametric peer (pairwise) bootstrap confidence intervals for the peer performance ratios (pipos/pizero/pineg)
• Added a reproducible Monte-Carlo validation script (system.file("scripts", "validation.R", package = "PeerPerformance")): checks the near-unbiasedness of pizero under the equal-performance null and the size/power of the modified Sharpe equality test
• Added a package vignette (“Luck-Corrected Peer Performance Analysis with PeerPerformance”), a pkgdown configuration, and a package CITATION entry
• Robustness: processControl now validates that scalar control values are single finite numbers / logicals; computePi checks the lambda length; targetPeerPerformance rejects non-whole funds; rollScreening validates by and only flags screen_beta for the alpha screen with factors; output fund names are preserved in as.data.frame and targetPeerPerformance
• Fixed a bug in sharpeScreening/msharpeScreening on unbalanced panels: the focal fund’s returns were indexed with the first peer’s missing-value mask (X[idx[, k], 1]) instead of the current pair’s (X[idx[, k], k]), which could inject NAs and yield NaN p-values for some pairs (reported by GitHub user NenoJo)
• Added targetPeerPerformance() (contributed by Murilo Andre Peres Pereira): screens a selected subset of funds against the whole universe; a convenience wrapper over the cross-group screening (*Screening(X[, funds], Y = X))
• Added a summary method for SCREENING objects (contributed by Murilo Andre Peres Pereira): ranked table, distribution of the measure, win/loss counts and top funds
• Added rollScreening(): rolling-window screening returning the time series of cross-sectionally averaged ratios (per factor when screen_beta = TRUE), with a plot method – the dynamic design of Ardia et al. (2022, 2023)
• plot on a cross-group screening omits the (within-group) percentile-rank diagonal; a single focal fund is shown as one stacked bar
• screen_beta can now also be set through control (e.g. control = list(screen_beta = TRUE)); the function argument still works and takes precedence
• Cross-group screening: alphaScreening, sharpeScreening and msharpeScreening gain a Y argument to screen each fund in X against a second peer group Y (a single focal fund versus a group is X a vector); columns of Y identical to the focal fund are excluded automatically
• Added as.data.frame method for SCREENING objects (tidy, one row per fund, or per fund/coefficient with screen_beta = TRUE)
• screen_beta = TRUE output now labels the coefficient rows (alpha + factor names), and exposureHeterogeneity() aggregates them into the factor exposure heterogeneity measure of Ardia et al. (2023), with a plot method
• Added print methods for the TESTING and SCREENING objects, and a plot method for the SCREENING object that reproduces the peer performance screening plot of Ardia and Boudt (2018)
• gammaPos and gammaNeg (default 0.4 and 0.6) are now exposed in the control list of the screening functions, controlling the one-sided thresholds used for the out- and underperformance counts
• Fixed the default number of bootstrap replications (nBoot = 499) when an empty control list is supplied
• Fixed the VAR(1) data-generating process in the optimal block-length routines (sharpeBlockSize, msharpeBlockSize): the lagged cross term in the second equation now uses the correct series
• sharpe() now counts observations with is.finite(), consistent with the other moment computations, when NA/NaN are present
• Moved run-time dependencies from Depends to Imports
• Documentation fixes (Sharpe 1994 reference, alphaTesting return values)

Version 2.3.2 (DA)

• Several fixes in documentation and good practices

Version 2.3.1 (DA)

• Doc fixed

Version 2.3.0 (DA,SL)

• Doc fixed
• alphaScreening now also outputs the betas
• Bug in lambda resampling fixed

Version 2.2.5 (DA)

• Doc fixed

Version 2.2.3 (DA)

• url fixed

Version 2.2.1 (DA)

• Switch to parallel package
• References updated

Version 2.1.4 (DA)

• Small fix in counting NA

Version 2.1.3 (DA)

• Documentation fixes

Version 2.1.2 (DA)

• Documentation fixes

Version 2.1.1 (DA)

• Documentation fixes
• First CRAN release

Version 2.1.0 (DA)

• New PeerPerformance documentation
• Compiler imported directly within function

Version 2.0.11 (DA)

• Examples added
• Block length not exported anymore

Version 2.0.10 (DA)

• Roxygen documentation
• testthat added
• Format of code

Version 2.0.9 (DA)

• Update CITATION and DESCRIPTION

Version 2.0.8 (DA)

• Small improvements with compiler
• Citations updated

Version 2.0.6 and 2.0.7 (DA)

• Various improvements
• Small fix in documentation

Version 2.0.5 (DA)

• Fix in documentation for modified Sharpe testing
• Small fix in pvalue computation by bootstrap (symmetric)

Version 2.0.4 (DA)

• Bug fix for alpha screening when NA are in the dataset
• Risk-free rate removed
• tstat used for attribution

Version 2.0.3 (DA)

• Major functions contain risk-free rates (zero by default)
• Documentation updated
• Adjustment factor robustified

Version 2.0.2 (DA)

• alphaScreening fixed
• alphaScreening now encompasses hac estimation with sandwich and lmtest
• citation file updated

Version 2.0.1 (DA)

• New package’s name
• New package’s number
• New package’s structure

Version 1-00.15 (DA)

• pi+ fixed
• Default settings for lambda = NULL

Version 1-00.14 (DA)

• Fix of errors in examples

Version 1-00.13 (DA)

• Control parameters for lambda data driven (NULL)
• Documentation updated
• Function for optimal lambda corrected and enhanced
• Function pizero and pi corrected

Version 1-00.01 (DA)

• First release
• Package includes (parallel) alpha and sharpe screening algorithms

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